dataprepare_monkey.Rmd
2024-08-19
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summary(cars)## speed dist
## Min. : 4.0 Min. : 2.00
## 1st Qu.:12.0 1st Qu.: 26.00
## Median :15.0 Median : 36.00
## Mean :15.4 Mean : 42.98
## 3rd Qu.:19.0 3rd Qu.: 56.00
## Max. :25.0 Max. :120.00.libPaths()## [1] "C:/Users/fxyin/AppData/Local/R/win-library/4.3"
## [2] "C:/Program Files/R/R-4.3.3/library"########import packages and seurat objects of dataset###########
suppressMessages(library(plotly))
suppressMessages(library(Seurat))
suppressMessages(library(dplyr))
suppressMessages(library(Matrix))
suppressMessages(library(gplots))
library(readxl)
library(devtools)## Loading required package: usethislibrary(clustree)## Loading required package: ggraph##
## Attaching package: 'ggraph'## The following object is masked from 'package:sp':
##
## geometrylibrary(RColorBrewer)
library(reshape)##
## Attaching package: 'reshape'## The following object is masked from 'package:Matrix':
##
## expand## The following object is masked from 'package:dplyr':
##
## rename## The following object is masked from 'package:plotly':
##
## renamelibrary(xlsx)
setwd("D:/xueying-work/afterPHD/Liu-lab/project1-embryo benchmarking")##load data
raw_list <- readRDS("D:/xueying-work/afterPHD/Liu-lab/project1-embryo benchmarking/dataset/monkey_list5.0.rds")
raw_list## $Sasaki_paper
## An object of class Seurat
## 28551 features across 34 samples within 1 assay
## Active assay: RNA (28551 features, 0 variable features)
## 1 layer present: counts
##
## $Nakamura_paper
## An object of class Seurat
## 28797 features across 421 samples within 1 assay
## Active assay: RNA (28797 features, 0 variable features)
## 1 layer present: counts
##
## $Zhai_2022
## An object of class Seurat
## 21584 features across 66702 samples within 1 assay
## Active assay: RNA (21584 features, 0 variable features)
## 7 layers present: counts.1.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.2.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Zhai_2022.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Zhai_2022.SeuratProject.SeuratProject.SeuratProject, counts.Zhai_2022.SeuratProject.SeuratProject, counts.Zhai_2022.SeuratProject, counts.Zhai_2022
##
## $Ma
## An object of class Seurat
## 29324 features across 1012681 samples within 1 assay
## Active assay: RNA (29324 features, 0 variable features)
## 21 layers present: counts.1.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.2.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject.SeuratProject, counts.Ma.SeuratProject.SeuratProject, counts.Ma.SeuratProject, counts.Ma
##
## $Ma_paper
## An object of class Seurat
## 30792 features across 1453 samples within 1 assay
## Active assay: RNA (30792 features, 0 variable features)
## 1 layer present: counts
##
## $Niu_paper
## An object of class Seurat
## 28592 features across 600 samples within 1 assay
## Active assay: RNA (28592 features, 0 variable features)
## 1 layer present: counts
##
## $Yang
## An object of class Seurat
## 21584 features across 16857 samples within 1 assay
## Active assay: RNA (21584 features, 0 variable features)
## 12 layers present: counts.1.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.2.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Yang.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Yang.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Yang.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Yang.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Yang.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Yang.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Yang.SeuratProject.SeuratProject.SeuratProject, counts.Yang.SeuratProject.SeuratProject, counts.Yang.SeuratProject, counts.Yang
##
## $Zhai_2023
## An object of class Seurat
## 29324 features across 1646849 samples within 1 assay
## Active assay: RNA (29324 features, 0 variable features)
## 44 layers present: counts.1.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, 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counts.Zhai_2023.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Zhai_2023.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Zhai_2023.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Zhai_2023.SeuratProject.SeuratProject.SeuratProject, counts.Zhai_2023.SeuratProject.SeuratProject, counts.Zhai_2023.SeuratProject, counts.Zhai_2023
##
## $Gong
## An object of class Seurat
## 21584 features across 153641 samples within 1 assay
## Active assay: RNA (21584 features, 0 variable features)
## 24 layers present: counts.1.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.2.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject.SeuratProject, counts.Gong.SeuratProject.SeuratProject, counts.Gong.SeuratProject, counts.Gong
##
## $Li
## An object of class Seurat
## 21584 features across 31874 samples within 1 assay
## Active assay: RNA (21584 features, 0 variable features)
## 2 layers present: counts.1, counts.2##data overview
#check cells by stage
age <- do.call(rbind, lapply(1:length(raw_list), function(x) {
df <- as.data.frame(table(raw_list[[x]]$stage) )
df$dataset <- names(raw_list[x])
return(df)
}))
#check table
print(age)## Var1 Freq dataset
## 1 E13 3 Sasaki_paper
## 2 E14 1 Sasaki_paper
## 3 E16 11 Sasaki_paper
## 4 E17 2 Sasaki_paper
## 5 E20 8 Sasaki_paper
## 6 E36m 6 Sasaki_paper
## 7 E47m 1 Sasaki_paper
## 8 E55m 2 Sasaki_paper
## 9 C6FF 9 Nakamura_paper
## 10 C6oF 14 Nakamura_paper
## 11 C9oF 8 Nakamura_paper
## 12 E06 43 Nakamura_paper
## 13 E07 35 Nakamura_paper
## 14 E08 95 Nakamura_paper
## 15 E09 20 Nakamura_paper
## 16 E13 46 Nakamura_paper
## 17 E14 62 Nakamura_paper
## 18 E16 36 Nakamura_paper
## 19 E17 53 Nakamura_paper
## 20 CS11-e1 11360 Zhai_2022
## 21 CS11-e2 14288 Zhai_2022
## 22 CS11-ys1 6859 Zhai_2022
## 23 CS8-e1 5892 Zhai_2022
## 24 CS8-e2 8958 Zhai_2022
## 25 CS9-e1 10024 Zhai_2022
## 26 CS9-e2 9321 Zhai_2022
## 27 day11 107647 Ma
## 28 day12 148279 Ma
## 29 Day12 50346 Ma
## 30 day13 48308 Ma
## 31 Day13 88477 Ma
## 32 day14 206006 Ma
## 33 day16 211473 Ma
## 34 day17 54193 Ma
## 35 Day17 97952 Ma
## 36 d.p.f.11 154 Ma_paper
## 37 d.p.f.12 249 Ma_paper
## 38 d.p.f.13 162 Ma_paper
## 39 d.p.f.14 322 Ma_paper
## 40 d.p.f.16 336 Ma_paper
## 41 d.p.f.17 230 Ma_paper
## 42 E09 36 Niu_paper
## 43 E11 57 Niu_paper
## 44 E13 96 Niu_paper
## 45 E15 121 Niu_paper
## 46 E17 140 Niu_paper
## 47 E19 66 Niu_paper
## 48 E20 84 Niu_paper
## 49 D10 2993 Yang
## 50 D12 3157 Yang
## 51 D14 10707 Yang
## 52 E16 329757 Zhai_2023
## 53 E18 137172 Zhai_2023
## 54 E20 248878 Zhai_2023
## 55 E21 184546 Zhai_2023
## 56 E22 155744 Zhai_2023
## 57 E23 294811 Zhai_2023
## 58 E24 102855 Zhai_2023
## 59 E25 193086 Zhai_2023
## 60 E18 21478 Gong
## 61 E19 16739 Gong
## 62 E20 14934 Gong
## 63 E21 11828 Gong
## 64 E22 11757 Gong
## 65 E23 39142 Gong
## 66 E24 20297 Gong
## 67 E25 17466 Gong
## 68 D17 10543 Li
## 69 D7 21331 Li##data organize
##remove Ma from the list
raw_list <- raw_list[!names(raw_list) %in% "Ma"]
##remove Zhai_2023 from the list
raw_list <- raw_list[!names(raw_list) %in% "Zhai_2023"]
##remove Zhai_2023 from the list
raw_list <- raw_list[!names(raw_list) %in% "Li"]
raw_list$Zhai_2022 <- JoinLayers(raw_list$Zhai_2022)
raw_list$Yang <- JoinLayers(raw_list$Yang)
raw_list$Gong <- JoinLayers(raw_list$Gong)
#filter cells with thresholds
for (i in seq_along(raw_list)) {
# Access the metadata
metadata <- raw_list[[i]]@meta.data
# Apply the filter to the metadata and identify cells to keep
cells_to_keep <- colnames(raw_list[[i]])[metadata$nFeature_RNA > 500 & metadata$nCount_RNA > 1000 & metadata$percent.mt < 15]
# Check if any cells meet the criteria
if (length(cells_to_keep) > 0) {
# Subset the Seurat object to keep only the desired cells
raw_list[[i]] <- subset(raw_list[[i]], cells = cells_to_keep)
} else {
message(paste("No cells found for raw_list element:", i))
}
}## No cells found for raw_list element: 4# Verify the result
print(raw_list)## $Sasaki_paper
## An object of class Seurat
## 28551 features across 34 samples within 1 assay
## Active assay: RNA (28551 features, 0 variable features)
## 1 layer present: counts
##
## $Nakamura_paper
## An object of class Seurat
## 28797 features across 421 samples within 1 assay
## Active assay: RNA (28797 features, 0 variable features)
## 1 layer present: counts
##
## $Zhai_2022
## An object of class Seurat
## 21584 features across 64575 samples within 1 assay
## Active assay: RNA (21584 features, 0 variable features)
## 1 layer present: counts
##
## $Ma_paper
## An object of class Seurat
## 30792 features across 1453 samples within 1 assay
## Active assay: RNA (30792 features, 0 variable features)
## 1 layer present: counts
##
## $Niu_paper
## An object of class Seurat
## 28592 features across 600 samples within 1 assay
## Active assay: RNA (28592 features, 0 variable features)
## 1 layer present: counts
##
## $Yang
## An object of class Seurat
## 21584 features across 15503 samples within 1 assay
## Active assay: RNA (21584 features, 0 variable features)
## 1 layer present: counts
##
## $Gong
## An object of class Seurat
## 21584 features across 134022 samples within 1 assay
## Active assay: RNA (21584 features, 0 variable features)
## 1 layer present: counts# check cell numbers
cell_counts <- data.frame(Dataset = integer(), Cell_Count = integer())
for (i in seq_along(raw_list)) {
# Get the number of cells in the current Seurat object
cell_count <- ncol(raw_list[[i]])
# Store the result in the data frame
cell_counts <- rbind(cell_counts, data.frame(Dataset = names(raw_list[i]), Cell_Count = cell_count))
}
# Print the table of cell counts
print(cell_counts)## Dataset Cell_Count
## 1 Sasaki_paper 34
## 2 Nakamura_paper 421
## 3 Zhai_2022 64575
## 4 Ma_paper 1453
## 5 Niu_paper 600
## 6 Yang 15503
## 7 Gong 134022##sasaki dataset
##check colnames
colnames(raw_list$Sasaki_paper)[1:5]## [1] "E20_MS08T78" "E20_MS08T79" "E20_MS08T81" "E20_MS08T82" "E20_MS08T83"ncol(raw_list$Sasaki_paper)## [1] 34##add annotation
sasaki_info <- read_excel("D:/xueying-work/afterPHD/Liu-lab/project1-embryo benchmarking/2024April/data/Monkey-Sasaki/mmc2 (1).xlsx")
#match colnames
df <- sasaki_info[which(sasaki_info$SampleID %in% rownames(raw_list$Sasaki_paper@meta.data)),]
df <- df[match(rownames(raw_list$Sasaki_paper@meta.data), df$SampleID),]
raw_list$Sasaki_paper@meta.data$anno <- df$Group
####check plots############
VlnPlot(raw_list$Sasaki_paper, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
VlnPlot(raw_list$Sasaki_paper, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
##remove E36m, E47m and E55m
raw_list$Sasaki_paper@meta.data$stage <- as.character(raw_list$Sasaki_paper@meta.data$stage)
meta_data <- raw_list$Sasaki_paper@meta.data
# Identify the cells to keep by creating a logical vector
cells_to_keep <- rownames(meta_data[!meta_data$stage %in% c("E36m", "E47m", "E55m"), ])
# Subset the Seurat object using the identified cells
raw_list$Sasaki_paper <- subset(raw_list$Sasaki_paper, cells = cells_to_keep)
meta <- raw_list$Sasaki_paper@meta.data
df2 <- as.data.frame(table(meta$stage, meta$anno))
##plot###n_cells
ggplot(data=df2, aes(x=Var1, y=Freq, fill=Var2)) +
geom_bar(stat="identity")+
theme(text=element_text(size=30))
##nakamura dataset
##check colnames
colnames(raw_list$Nakamura_paper)[1:5]## [1] "C6FF_MS11T85" "C6FF_MS11T86" "C6FF_MS11T87" "C6FF_MS11T88" "C6FF_MS11T89"ncol(raw_list$Nakamura_paper)## [1] 421##add annotation
Nakamura_info <- read_excel("D:/xueying-work/afterPHD/Liu-lab/project1-embryo benchmarking/2024April/data/Monkey-Nakamura/Nakamura.xlsx")## New names:
## • `` -> `...2`
## • `` -> `...3`
## • `` -> `...4`
## • `` -> `...5`
## • `` -> `...6`
## • `` -> `...7`
## • `` -> `...8`
## • `` -> `...9`
## • `` -> `...10`
## • `` -> `...11`
## • `` -> `...12`
## • `` -> `...13`
## • `` -> `...14`colnames(Nakamura_info) <-Nakamura_info[1,]
Nakamura_info <- Nakamura_info[-1,]
Nakamura_info <- Nakamura_info[which(Nakamura_info$Species=="Cynomolgus Monkey"),]
#match colnames
anno_table <- Nakamura_info
dataset <- raw_list$Nakamura_paper
length(Nakamura_info$SampleID %in% colnames(Nakamura_info))## [1] 421df <- Nakamura_info[match(rownames(raw_list$Nakamura_paper@meta.data), Nakamura_info$SampleID),]
raw_list$Nakamura_paper@meta.data$anno <- df$Group
dataset <- raw_list$Nakamura_paper
dataset <- subset(dataset, subset= anno != "cyESCFF" & anno != "cyESCoF" )
raw_list$Nakamura_paper <- dataset
####check plots############
VlnPlot(raw_list$Nakamura_paper, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
raw_list$Nakamura_paper@meta.data$stage <- as.character(raw_list$Nakamura_paper@meta.data$stage)
raw_list$Nakamura_paper@meta.data$stage <- as.factor(raw_list$Nakamura_paper@meta.data$stage)
levels(raw_list$Nakamura_paper@meta.data$stage)## [1] "E06" "E07" "E08" "E09" "E13" "E14" "E16" "E17"meta <- raw_list$Nakamura_paper@meta.data
df2 <- as.data.frame(table(meta$stage, meta$anno))
##plot###n_cells before QC
ggplot(data=df2, aes(x=Var1, y=Freq, fill=Var2)) +
geom_bar(stat="identity")+
theme(text=element_text(size=30))
#Ma paper
raw_list$Ma_paper$embryo <- raw_list$Ma_paper$orig.ident
raw_list$Ma_paper$orig.ident <- "Ma"
raw_list$Ma_paper[["percent.mt"]] <- PercentageFeatureSet(raw_list$Ma_paper, pattern = "^MT.")
VlnPlot(raw_list$Ma_paper, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), group.by = "orig.ident", ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
meta <- raw_list$Ma_paper@meta.data
df2 <- as.data.frame(table(meta$stage, meta$anno))
##plot###n_cells before QC
ggplot(data=df2, aes(x=Var1, y=Freq, fill=Var2)) +
geom_bar(stat="identity")+
theme(text=element_text(size=30))
##Niu paper #have problem with gene names so fix this first
genes <- as.data.frame(rownames(raw_list$Niu_paper))
genes_2 <- genes[grep("ENSMFAG", genes[,1]),,drop=FALSE]
genes.rest <- genes[-grep("ENSMFAG", genes[,1]),,drop=FALSE]
######use both mfascicularis and hsapiens biomart#######
library(biomaRt)
ensembl <- useEnsembl(biomart = "ensembl", mirror = "asia") # Use the Asia mirror## Ensembl site unresponsive, trying www mirrorlistEnsembl()## biomart version
## 1 genes Ensembl Genes 112
## 2 mouse_strains Mouse strains 112
## 3 snps Ensembl Variation 112
## 4 regulation Ensembl Regulation 112listDatasets(ensembl)## dataset
## 1 abrachyrhynchus_gene_ensembl
## 2 acalliptera_gene_ensembl
## 3 acarolinensis_gene_ensembl
## 4 acchrysaetos_gene_ensembl
## 5 acitrinellus_gene_ensembl
## 6 amelanoleuca_gene_ensembl
## 7 amexicanus_gene_ensembl
## 8 anancymaae_gene_ensembl
## 9 aocellaris_gene_ensembl
## 10 apercula_gene_ensembl
## 11 aplatyrhynchos_gene_ensembl
## 12 apolyacanthus_gene_ensembl
## 13 applatyrhynchos_gene_ensembl
## 14 atestudineus_gene_ensembl
## 15 bbbison_gene_ensembl
## 16 bgrunniens_gene_ensembl
## 17 bihybrid_gene_ensembl
## 18 bmusculus_gene_ensembl
## 19 bmutus_gene_ensembl
## 20 bsplendens_gene_ensembl
## 21 btaurus_gene_ensembl
## 22 cabingdonii_gene_ensembl
## 23 catys_gene_ensembl
## 24 cauratus_gene_ensembl
## 25 cccarpio_gene_ensembl
## 26 cdromedarius_gene_ensembl
## 27 celegans_gene_ensembl
## 28 cgchok1gshd_gene_ensembl
## 29 cgobio_gene_ensembl
## 30 charengus_gene_ensembl
## 31 chircus_gene_ensembl
## 32 choffmanni_gene_ensembl
## 33 chyarkandensis_gene_ensembl
## 34 cimitator_gene_ensembl
## 35 cintestinalis_gene_ensembl
## 36 cjacchus_gene_ensembl
## 37 cjaponica_gene_ensembl
## 38 clanigera_gene_ensembl
## 39 cldingo_gene_ensembl
## 40 clfamiliaris_gene_ensembl
## 41 clumpus_gene_ensembl
## 42 cmilii_gene_ensembl
## 43 cpbellii_gene_ensembl
## 44 cporcellus_gene_ensembl
## 45 cporosus_gene_ensembl
## 46 csabaeus_gene_ensembl
## 47 csavignyi_gene_ensembl
## 48 csemilaevis_gene_ensembl
## 49 csyrichta_gene_ensembl
## 50 cvariegatus_gene_ensembl
## 51 cwagneri_gene_ensembl
## 52 dclupeoides_gene_ensembl
## 53 dlabrax_gene_ensembl
## 54 dleucas_gene_ensembl
## 55 dmelanogaster_gene_ensembl
## 56 dnovemcinctus_gene_ensembl
## 57 dordii_gene_ensembl
## 58 drerio_gene_ensembl
## 59 easinus_gene_ensembl
## 60 eburgeri_gene_ensembl
## 61 ecaballus_gene_ensembl
## 62 ecalabaricus_gene_ensembl
## 63 eelectricus_gene_ensembl
## 64 eeuropaeus_gene_ensembl
## 65 elucius_gene_ensembl
## 66 etelfairi_gene_ensembl
## 67 falbicollis_gene_ensembl
## 68 fcatus_gene_ensembl
## 69 fheteroclitus_gene_ensembl
## 70 gaculeatus_gene_ensembl
## 71 gevgoodei_gene_ensembl
## 72 gfortis_gene_ensembl
## 73 ggallus_gene_ensembl
## 74 ggorilla_gene_ensembl
## 75 gmorhua_gene_ensembl
## 76 hburtoni_gene_ensembl
## 77 hcomes_gene_ensembl
## 78 hgfemale_gene_ensembl
## 79 hhucho_gene_ensembl
## 80 hsapiens_gene_ensembl
## 81 ipunctatus_gene_ensembl
## 82 itridecemlineatus_gene_ensembl
## 83 jjaculus_gene_ensembl
## 84 kmarmoratus_gene_ensembl
## 85 lafricana_gene_ensembl
## 86 lbergylta_gene_ensembl
## 87 lcalcarifer_gene_ensembl
## 88 lchalumnae_gene_ensembl
## 89 lcrocea_gene_ensembl
## 90 llaticaudata_gene_ensembl
## 91 lleishanense_gene_ensembl
## 92 loculatus_gene_ensembl
## 93 marmatus_gene_ensembl
## 94 mauratus_gene_ensembl
## 95 mcaroli_gene_ensembl
## 96 mdomestica_gene_ensembl
## 97 mfascicularis_gene_ensembl
## 98 mgallopavo_gene_ensembl
## 99 mleucophaeus_gene_ensembl
## 100 mlucifugus_gene_ensembl
## 101 mmmarmota_gene_ensembl
## 102 mmonoceros_gene_ensembl
## 103 mmoschiferus_gene_ensembl
## 104 mmulatta_gene_ensembl
## 105 mmurdjan_gene_ensembl
## 106 mmurinus_gene_ensembl
## 107 mmusculus_gene_ensembl
## 108 mnemestrina_gene_ensembl
## 109 mochrogaster_gene_ensembl
## 110 mpahari_gene_ensembl
## 111 mpfuro_gene_ensembl
## 112 mspicilegus_gene_ensembl
## 113 mspretus_gene_ensembl
## 114 mzebra_gene_ensembl
## 115 nbrichardi_gene_ensembl
## 116 neugenii_gene_ensembl
## 117 nfurzeri_gene_ensembl
## 118 ngalili_gene_ensembl
## 119 nleucogenys_gene_ensembl
## 120 nnaja_gene_ensembl
## 121 nscutatus_gene_ensembl
## 122 nvison_gene_ensembl
## 123 oanatinus_gene_ensembl
## 124 oarambouillet_gene_ensembl
## 125 oaries_gene_ensembl
## 126 ocuniculus_gene_ensembl
## 127 odegus_gene_ensembl
## 128 ogarnettii_gene_ensembl
## 129 ojavanicus_gene_ensembl
## 130 okisutch_gene_ensembl
## 131 olatipes_gene_ensembl
## 132 omelastigma_gene_ensembl
## 133 omykiss_gene_ensembl
## 134 oniloticus_gene_ensembl
## 135 oprinceps_gene_ensembl
## 136 osinensis_gene_ensembl
## 137 otshawytscha_gene_ensembl
## 138 pabelii_gene_ensembl
## 139 panubis_gene_ensembl
## 140 pcapensis_gene_ensembl
## 141 pcatodon_gene_ensembl
## 142 pcinereus_gene_ensembl
## 143 pcoquereli_gene_ensembl
## 144 pformosa_gene_ensembl
## 145 pkingsleyae_gene_ensembl
## 146 platipinna_gene_ensembl
## 147 pleo_gene_ensembl
## 148 pmajor_gene_ensembl
## 149 pmarinus_gene_ensembl
## 150 pmbairdii_gene_ensembl
## 151 pmuralis_gene_ensembl
## 152 pnattereri_gene_ensembl
## 153 pnyererei_gene_ensembl
## 154 ppaniscus_gene_ensembl
## 155 ppardus_gene_ensembl
## 156 preticulata_gene_ensembl
## 157 psimus_gene_ensembl
## 158 psinensis_gene_ensembl
## 159 psinus_gene_ensembl
## 160 ptaltaica_gene_ensembl
## 161 ptextilis_gene_ensembl
## 162 ptroglodytes_gene_ensembl
## 163 pvampyrus_gene_ensembl
## 164 rbieti_gene_ensembl
## 165 rferrumequinum_gene_ensembl
## 166 rnorvegicus_gene_ensembl
## 167 rroxellana_gene_ensembl
## 168 saraneus_gene_ensembl
## 169 saurata_gene_ensembl
## 170 sbboliviensis_gene_ensembl
## 171 scanaria_gene_ensembl
## 172 scaustralis_gene_ensembl
## 173 scerevisiae_gene_ensembl
## 174 sdumerili_gene_ensembl
## 175 sformosus_gene_ensembl
## 176 shabroptila_gene_ensembl
## 177 sharrisii_gene_ensembl
## 178 sldorsalis_gene_ensembl
## 179 slucioperca_gene_ensembl
## 180 smaximus_gene_ensembl
## 181 smerianae_gene_ensembl
## 182 spartitus_gene_ensembl
## 183 spunctatus_gene_ensembl
## 184 ssalar_gene_ensembl
## 185 ssbamei_gene_ensembl
## 186 ssberkshire_gene_ensembl
## 187 sscrofa_gene_ensembl
## 188 sshampshire_gene_ensembl
## 189 ssjinhua_gene_ensembl
## 190 sslandrace_gene_ensembl
## 191 sslargewhite_gene_ensembl
## 192 ssmeishan_gene_ensembl
## 193 sspietrain_gene_ensembl
## 194 ssrongchang_gene_ensembl
## 195 sstibetan_gene_ensembl
## 196 ssusmarc_gene_ensembl
## 197 sswuzhishan_gene_ensembl
## 198 strutta_gene_ensembl
## 199 svulgaris_gene_ensembl
## 200 tbelangeri_gene_ensembl
## 201 tctriunguis_gene_ensembl
## 202 tguttata_gene_ensembl
## 203 tnigroviridis_gene_ensembl
## 204 trubripes_gene_ensembl
## 205 ttruncatus_gene_ensembl
## 206 uamericanus_gene_ensembl
## 207 umaritimus_gene_ensembl
## 208 uparryii_gene_ensembl
## 209 vpacos_gene_ensembl
## 210 vursinus_gene_ensembl
## 211 vvulpes_gene_ensembl
## 212 xcouchianus_gene_ensembl
## 213 xmaculatus_gene_ensembl
## 214 xtropicalis_gene_ensembl
## description
## 1 Pink-footed goose genes (ASM259213v1)
## 2 Eastern happy genes (fAstCal1.3)
## 3 Green anole genes (AnoCar2.0v2)
## 4 Golden eagle genes (bAquChr1.2)
## 5 Midas cichlid genes (Midas_v5)
## 6 Giant panda genes (ASM200744v2)
## 7 Mexican tetra genes (Astyanax_mexicanus-2.0)
## 8 Ma's night monkey genes (Anan_2.0)
## 9 Clown anemonefish genes (ASM2253959v1)
## 10 Orange clownfish genes (Nemo_v1)
## 11 Mallard genes (ASM874695v1)
## 12 Spiny chromis genes (ASM210954v1)
## 13 Duck genes (CAU_duck1.0)
## 14 Climbing perch genes (fAnaTes1.3)
## 15 American bison genes (Bison_UMD1.0)
## 16 Domestic yak genes (LU_Bosgru_v3.0)
## 17 Hybrid - Bos Indicus genes (UOA_Brahman_1)
## 18 Blue whale genes (mBalMus1.v2)
## 19 Wild yak genes (BosGru_v2.0)
## 20 Siamese fighting fish genes (fBetSpl5.2)
## 21 Cow genes (ARS-UCD1.3)
## 22 Abingdon island giant tortoise genes (ASM359739v1)
## 23 Sooty mangabey genes (Caty_1.0)
## 24 Goldfish genes (ASM336829v1)
## 25 Common carp genes (Cypcar_WagV4.0)
## 26 Arabian camel genes (CamDro2)
## 27 Caenorhabditis elegans (Nematode, N2) genes (WBcel235)
## 28 Chinese hamster CHOK1GS genes (CHOK1GS_HDv1)
## 29 Channel bull blenny genes (fCotGob3.1)
## 30 Atlantic herring genes (Ch_v2.0.2v2)
## 31 Goat genes (ARS1)
## 32 Sloth genes (choHof1)
## 33 Yarkand deer genes (CEY_v1)
## 34 Panamanian white-faced capuchin genes (Cebus_imitator-1.0)
## 35 C.intestinalis genes (KH)
## 36 White-tufted-ear marmoset genes (mCalJac1.pat.X)
## 37 Japanese quail genes (Coturnix_japonica_2.0)
## 38 Long-tailed chinchilla genes (ChiLan1.0)
## 39 Dingo genes (ASM325472v1)
## 40 Dog genes (ROS_Cfam_1.0)
## 41 Lumpfish genes (fCycLum1.pri)
## 42 Elephant shark genes (Callorhinchus_milii-6.1.3)
## 43 Painted turtle genes (Chrysemys_picta_bellii-3.0.3)
## 44 Guinea Pig genes (Cavpor3.0)
## 45 Australian saltwater crocodile genes (CroPor_comp1)
## 46 Vervet-AGM genes (ChlSab1.1)
## 47 C.savignyi genes (CSAV 2.0)
## 48 Tongue sole genes (Cse_v1.0)
## 49 Tarsier genes (Tarsius_syrichta-2.0.1)
## 50 Sheepshead minnow genes (C_variegatus-1.0)
## 51 Chacoan peccary genes (CatWag_v2_BIUU_UCD)
## 52 Denticle herring genes (fDenClu1.2)
## 53 European seabass genes (dlabrax2021)
## 54 Beluga whale genes (ASM228892v3)
## 55 Drosophila melanogaster (Fruit fly) genes (BDGP6.46)
## 56 Armadillo genes (Dasnov3.0)
## 57 Kangaroo rat genes (Dord_2.0)
## 58 Zebrafish genes (GRCz11)
## 59 Donkey genes (ASM1607732v2)
## 60 Hagfish genes (Eburgeri_3.2)
## 61 Horse genes (EquCab3.0)
## 62 Reedfish genes (fErpCal1.1)
## 63 Electric eel genes (fEleEle1.pri)
## 64 Hedgehog genes (eriEur1)
## 65 Northern pike genes (fEsoLuc1.pri)
## 66 Lesser hedgehog tenrec genes (TENREC)
## 67 Collared flycatcher genes (FicAlb1.5)
## 68 Cat genes (Felis_catus_9.0)
## 69 Mummichog genes (Fundulus_heteroclitus-3.0.2)
## 70 Stickleback genes (GAculeatus_UGA_version5)
## 71 Goodes thornscrub tortoise genes (rGopEvg1_v1.p)
## 72 Medium ground-finch genes (GeoFor_1.0)
## 73 Chicken genes (bGalGal1.mat.broiler.GRCg7b)
## 74 Gorilla genes (gorGor4)
## 75 Atlantic cod genes (gadMor3.0)
## 76 Burton's mouthbrooder genes (AstBur1.0)
## 77 Tiger tail seahorse genes (H_comes_QL1_v1)
## 78 Naked mole-rat female genes (Naked_mole-rat_maternal)
## 79 Huchen genes (ASM331708v1)
## 80 Human genes (GRCh38.p14)
## 81 Channel catfish genes (ASM400665v3)
## 82 Squirrel genes (SpeTri2.0)
## 83 Lesser Egyptian jerboa genes (JacJac1.0)
## 84 Mangrove rivulus genes (ASM164957v1)
## 85 Elephant genes (Loxafr3.0)
## 86 Ballan wrasse genes (BallGen_V1)
## 87 Barramundi perch genes (ASB_HGAPassembly_v1)
## 88 Coelacanth genes (LatCha1)
## 89 Large yellow croaker genes (L_crocea_2.0)
## 90 Blue-ringed sea krait genes (latLat_1.0)
## 91 Leishan spiny toad genes (ASM966780v1)
## 92 Spotted gar genes (LepOcu1)
## 93 Zig-zag eel genes (fMasArm1.2)
## 94 Golden Hamster genes (MesAur1.0)
## 95 Ryukyu mouse genes (CAROLI_EIJ_v1.1)
## 96 Opossum genes (ASM229v1)
## 97 Crab-eating macaque genes (Macaca_fascicularis_6.0)
## 98 Turkey genes (Turkey_5.1)
## 99 Drill genes (Mleu.le_1.0)
## 100 Microbat genes (Myoluc2.0)
## 101 Alpine marmot genes (marMar2.1)
## 102 Narwhal genes (NGI_Narwhal_1)
## 103 Siberian musk deer genes (MosMos_v2_BIUU_UCD)
## 104 Macaque genes (Mmul_10)
## 105 Pinecone soldierfish genes (fMyrMur1.1)
## 106 Mouse Lemur genes (Mmur_3.0)
## 107 Mouse genes (GRCm39)
## 108 Pig-tailed macaque genes (Mnem_1.0)
## 109 Prairie vole genes (MicOch1.0)
## 110 Shrew mouse genes (PAHARI_EIJ_v1.1)
## 111 Ferret genes (MusPutFur1.0)
## 112 Steppe mouse genes (MUSP714)
## 113 Algerian mouse genes (SPRET_EiJ_v1)
## 114 Zebra mbuna genes (M_zebra_UMD2a)
## 115 Lyretail cichlid genes (NeoBri1.0)
## 116 Wallaby genes (Meug_1.0)
## 117 Turquoise killifish genes (Nfu_20140520)
## 118 Upper Galilee mountains blind mole rat genes (S.galili_v1.0)
## 119 Gibbon genes (Nleu_3.0)
## 120 Indian cobra genes (Nana_v5)
## 121 Mainland tiger snake genes (TS10Xv2-PRI)
## 122 American mink genes (NNQGG.v01)
## 123 Platypus genes (mOrnAna1.p.v1)
## 124 Sheep genes (ARS-UI_Ramb_v2.0)
## 125 Sheep (texel) genes (Oar_v3.1)
## 126 Rabbit genes (OryCun2.0)
## 127 Degu genes (OctDeg1.0)
## 128 Bushbaby genes (OtoGar3)
## 129 Javanese ricefish genes (OJAV_1.1)
## 130 Coho salmon genes (Okis_V2)
## 131 Japanese medaka HdrR genes (ASM223467v1)
## 132 Indian medaka genes (Om_v0.7.RACA)
## 133 Rainbow trout genes (USDA_OmykA_1.1)
## 134 Nile tilapia genes (O_niloticus_UMD_NMBU)
## 135 Pika genes (OchPri2.0-Ens)
## 136 Chinese medaka genes (ASM858656v1)
## 137 Chinook salmon genes (Otsh_v2.0)
## 138 Sumatran orangutan genes (Susie_PABv2)
## 139 Olive baboon genes (Panubis1.0)
## 140 Hyrax genes (proCap1)
## 141 Sperm whale genes (ASM283717v2)
## 142 Koala genes (phaCin_unsw_v4.1)
## 143 Coquerel's sifaka genes (Pcoq_1.0)
## 144 Amazon molly genes (Poecilia_formosa-5.1.2)
## 145 Paramormyrops kingsleyae genes (PKINGS_0.1)
## 146 Sailfin molly genes (P_latipinna-1.0)
## 147 Lion genes (PanLeo1.0)
## 148 Great Tit genes (Parus_major1.1)
## 149 Lamprey genes (Pmarinus_7.0)
## 150 Northern American deer mouse genes (HU_Pman_2.1)
## 151 Common wall lizard genes (PodMur_1.0)
## 152 Red-bellied piranha genes (fPygNat1.pri)
## 153 Makobe Island cichlid genes (PunNye1.0)
## 154 Bonobo genes (panpan1.1)
## 155 Leopard genes (PanPar1.0)
## 156 Guppy genes (Guppy_female_1.0_MT)
## 157 Greater bamboo lemur genes (Prosim_1.0)
## 158 Chinese softshell turtle genes (PelSin_1.0)
## 159 Vaquita genes (mPhoSin1.pri)
## 160 Tiger genes (PanTig1.0)
## 161 Eastern brown snake genes (EBS10Xv2-PRI)
## 162 Chimpanzee genes (Pan_tro_3.0)
## 163 Megabat genes (pteVam1)
## 164 Black snub-nosed monkey genes (ASM169854v1)
## 165 Greater horseshoe bat genes (mRhiFer1_v1.p)
## 166 Rat genes (mRatBN7.2)
## 167 Golden snub-nosed monkey genes (Rrox_v1)
## 168 Shrew genes (sorAra1)
## 169 Gilthead seabream genes (fSpaAur1.1)
## 170 Bolivian squirrel monkey genes (SaiBol1.0)
## 171 Common canary genes (SCA1)
## 172 African ostrich genes (ASM69896v1)
## 173 Saccharomyces cerevisiae genes (R64-1-1)
## 174 Greater amberjack genes (Sdu_1.0)
## 175 Asian bonytongue genes (fSclFor1.1)
## 176 Kakapo genes (bStrHab1_v1.p)
## 177 Tasmanian devil genes (mSarHar1.11)
## 178 Yellowtail amberjack genes (Sedor1)
## 179 Pike-perch genes (SLUC_FBN_1)
## 180 Turbot genes (ASM1334776v1)
## 181 Argentine black and white tegu genes (HLtupMer3)
## 182 Bicolor damselfish genes (Stegastes_partitus-1.0.2)
## 183 Tuatara genes (ASM311381v1)
## 184 Atlantic salmon genes (Ssal_v3.1)
## 185 Pig - Bamei genes (Bamei_pig_v1)
## 186 Pig - Berkshire genes (Berkshire_pig_v1)
## 187 Pig genes (Sscrofa11.1)
## 188 Pig - Hampshire genes (Hampshire_pig_v1)
## 189 Pig - Jinhua genes (Jinhua_pig_v1)
## 190 Pig - Landrace genes (Landrace_pig_v1)
## 191 Pig - Largewhite genes (Large_White_v1)
## 192 Pig - Meishan genes (Meishan_pig_v1)
## 193 Pig - Pietrain genes (Pietrain_pig_v1)
## 194 Pig - Rongchang genes (Rongchang_pig_v1)
## 195 Pig - Tibetan genes (Tibetan_Pig_v2)
## 196 Pig USMARC genes (USMARCv1.0)
## 197 Pig - Wuzhishan genes (minipig_v1.0)
## 198 Brown trout genes (fSalTru1.1)
## 199 Eurasian red squirrel genes (mSciVul1.1)
## 200 Tree Shrew genes (tupBel1)
## 201 Three-toed box turtle genes (T_m_triunguis-2.0)
## 202 Zebra finch genes (bTaeGut1_v1.p)
## 203 Tetraodon genes (TETRAODON 8.0)
## 204 Fugu genes (fTakRub1.2)
## 205 Dolphin genes (turTru1)
## 206 American black bear genes (ASM334442v1)
## 207 Polar bear genes (UrsMar_1.0)
## 208 Arctic ground squirrel genes (ASM342692v1)
## 209 Alpaca genes (vicPac1)
## 210 Common wombat genes (bare-nosed_wombat_genome_assembly)
## 211 Red fox genes (VulVul2.2)
## 212 Monterrey platyfish genes (Xiphophorus_couchianus-4.0.1)
## 213 Platyfish genes (X_maculatus-5.0-male)
## 214 Tropical clawed frog genes (UCB_Xtro_10.0)
## version
## 1 ASM259213v1
## 2 fAstCal1.3
## 3 AnoCar2.0v2
## 4 bAquChr1.2
## 5 Midas_v5
## 6 ASM200744v2
## 7 Astyanax_mexicanus-2.0
## 8 Anan_2.0
## 9 ASM2253959v1
## 10 Nemo_v1
## 11 ASM874695v1
## 12 ASM210954v1
## 13 CAU_duck1.0
## 14 fAnaTes1.3
## 15 Bison_UMD1.0
## 16 LU_Bosgru_v3.0
## 17 UOA_Brahman_1
## 18 mBalMus1.v2
## 19 BosGru_v2.0
## 20 fBetSpl5.2
## 21 ARS-UCD1.3
## 22 ASM359739v1
## 23 Caty_1.0
## 24 ASM336829v1
## 25 Cypcar_WagV4.0
## 26 CamDro2
## 27 WBcel235
## 28 CHOK1GS_HDv1
## 29 fCotGob3.1
## 30 Ch_v2.0.2v2
## 31 ARS1
## 32 choHof1
## 33 CEY_v1
## 34 Cebus_imitator-1.0
## 35 KH
## 36 mCalJac1.pat.X
## 37 Coturnix_japonica_2.0
## 38 ChiLan1.0
## 39 ASM325472v1
## 40 ROS_Cfam_1.0
## 41 fCycLum1.pri
## 42 Callorhinchus_milii-6.1.3
## 43 Chrysemys_picta_bellii-3.0.3
## 44 Cavpor3.0
## 45 CroPor_comp1
## 46 ChlSab1.1
## 47 CSAV 2.0
## 48 Cse_v1.0
## 49 Tarsius_syrichta-2.0.1
## 50 C_variegatus-1.0
## 51 CatWag_v2_BIUU_UCD
## 52 fDenClu1.2
## 53 dlabrax2021
## 54 ASM228892v3
## 55 BDGP6.46
## 56 Dasnov3.0
## 57 Dord_2.0
## 58 GRCz11
## 59 ASM1607732v2
## 60 Eburgeri_3.2
## 61 EquCab3.0
## 62 fErpCal1.1
## 63 fEleEle1.pri
## 64 eriEur1
## 65 fEsoLuc1.pri
## 66 TENREC
## 67 FicAlb1.5
## 68 Felis_catus_9.0
## 69 Fundulus_heteroclitus-3.0.2
## 70 GAculeatus_UGA_version5
## 71 rGopEvg1_v1.p
## 72 GeoFor_1.0
## 73 bGalGal1.mat.broiler.GRCg7b
## 74 gorGor4
## 75 gadMor3.0
## 76 AstBur1.0
## 77 H_comes_QL1_v1
## 78 Naked_mole-rat_maternal
## 79 ASM331708v1
## 80 GRCh38.p14
## 81 ASM400665v3
## 82 SpeTri2.0
## 83 JacJac1.0
## 84 ASM164957v1
## 85 Loxafr3.0
## 86 BallGen_V1
## 87 ASB_HGAPassembly_v1
## 88 LatCha1
## 89 L_crocea_2.0
## 90 latLat_1.0
## 91 ASM966780v1
## 92 LepOcu1
## 93 fMasArm1.2
## 94 MesAur1.0
## 95 CAROLI_EIJ_v1.1
## 96 ASM229v1
## 97 Macaca_fascicularis_6.0
## 98 Turkey_5.1
## 99 Mleu.le_1.0
## 100 Myoluc2.0
## 101 marMar2.1
## 102 NGI_Narwhal_1
## 103 MosMos_v2_BIUU_UCD
## 104 Mmul_10
## 105 fMyrMur1.1
## 106 Mmur_3.0
## 107 GRCm39
## 108 Mnem_1.0
## 109 MicOch1.0
## 110 PAHARI_EIJ_v1.1
## 111 MusPutFur1.0
## 112 MUSP714
## 113 SPRET_EiJ_v1
## 114 M_zebra_UMD2a
## 115 NeoBri1.0
## 116 Meug_1.0
## 117 Nfu_20140520
## 118 S.galili_v1.0
## 119 Nleu_3.0
## 120 Nana_v5
## 121 TS10Xv2-PRI
## 122 NNQGG.v01
## 123 mOrnAna1.p.v1
## 124 ARS-UI_Ramb_v2.0
## 125 Oar_v3.1
## 126 OryCun2.0
## 127 OctDeg1.0
## 128 OtoGar3
## 129 OJAV_1.1
## 130 Okis_V2
## 131 ASM223467v1
## 132 Om_v0.7.RACA
## 133 USDA_OmykA_1.1
## 134 O_niloticus_UMD_NMBU
## 135 OchPri2.0-Ens
## 136 ASM858656v1
## 137 Otsh_v2.0
## 138 Susie_PABv2
## 139 Panubis1.0
## 140 proCap1
## 141 ASM283717v2
## 142 phaCin_unsw_v4.1
## 143 Pcoq_1.0
## 144 Poecilia_formosa-5.1.2
## 145 PKINGS_0.1
## 146 P_latipinna-1.0
## 147 PanLeo1.0
## 148 Parus_major1.1
## 149 Pmarinus_7.0
## 150 HU_Pman_2.1
## 151 PodMur_1.0
## 152 fPygNat1.pri
## 153 PunNye1.0
## 154 panpan1.1
## 155 PanPar1.0
## 156 Guppy_female_1.0_MT
## 157 Prosim_1.0
## 158 PelSin_1.0
## 159 mPhoSin1.pri
## 160 PanTig1.0
## 161 EBS10Xv2-PRI
## 162 Pan_tro_3.0
## 163 pteVam1
## 164 ASM169854v1
## 165 mRhiFer1_v1.p
## 166 mRatBN7.2
## 167 Rrox_v1
## 168 sorAra1
## 169 fSpaAur1.1
## 170 SaiBol1.0
## 171 SCA1
## 172 ASM69896v1
## 173 R64-1-1
## 174 Sdu_1.0
## 175 fSclFor1.1
## 176 bStrHab1_v1.p
## 177 mSarHar1.11
## 178 Sedor1
## 179 SLUC_FBN_1
## 180 ASM1334776v1
## 181 HLtupMer3
## 182 Stegastes_partitus-1.0.2
## 183 ASM311381v1
## 184 Ssal_v3.1
## 185 Bamei_pig_v1
## 186 Berkshire_pig_v1
## 187 Sscrofa11.1
## 188 Hampshire_pig_v1
## 189 Jinhua_pig_v1
## 190 Landrace_pig_v1
## 191 Large_White_v1
## 192 Meishan_pig_v1
## 193 Pietrain_pig_v1
## 194 Rongchang_pig_v1
## 195 Tibetan_Pig_v2
## 196 USMARCv1.0
## 197 minipig_v1.0
## 198 fSalTru1.1
## 199 mSciVul1.1
## 200 tupBel1
## 201 T_m_triunguis-2.0
## 202 bTaeGut1_v1.p
## 203 TETRAODON 8.0
## 204 fTakRub1.2
## 205 turTru1
## 206 ASM334442v1
## 207 UrsMar_1.0
## 208 ASM342692v1
## 209 vicPac1
## 210 bare-nosed_wombat_genome_assembly
## 211 VulVul2.2
## 212 Xiphophorus_couchianus-4.0.1
## 213 X_maculatus-5.0-male
## 214 UCB_Xtro_10.0listEnsemblArchives()## name date url version
## 1 Ensembl GRCh37 Feb 2014 https://grch37.ensembl.org GRCh37
## 2 Ensembl 112 May 2024 https://may2024.archive.ensembl.org 112
## 3 Ensembl 111 Jan 2024 https://jan2024.archive.ensembl.org 111
## 4 Ensembl 110 Jul 2023 https://jul2023.archive.ensembl.org 110
## 5 Ensembl 109 Feb 2023 https://feb2023.archive.ensembl.org 109
## 6 Ensembl 108 Oct 2022 https://oct2022.archive.ensembl.org 108
## 7 Ensembl 107 Jul 2022 https://jul2022.archive.ensembl.org 107
## 8 Ensembl 106 Apr 2022 https://apr2022.archive.ensembl.org 106
## 9 Ensembl 105 Dec 2021 https://dec2021.archive.ensembl.org 105
## 10 Ensembl 104 May 2021 https://may2021.archive.ensembl.org 104
## 11 Ensembl 103 Feb 2021 https://feb2021.archive.ensembl.org 103
## 12 Ensembl 102 Nov 2020 https://nov2020.archive.ensembl.org 102
## 13 Ensembl 101 Aug 2020 https://aug2020.archive.ensembl.org 101
## 14 Ensembl 100 Apr 2020 https://apr2020.archive.ensembl.org 100
## 15 Ensembl 99 Jan 2020 https://jan2020.archive.ensembl.org 99
## 16 Ensembl 98 Sep 2019 https://sep2019.archive.ensembl.org 98
## 17 Ensembl 97 Jul 2019 https://jul2019.archive.ensembl.org 97
## 18 Ensembl 80 May 2015 https://may2015.archive.ensembl.org 80
## 19 Ensembl 77 Oct 2014 https://oct2014.archive.ensembl.org 77
## 20 Ensembl 75 Feb 2014 https://feb2014.archive.ensembl.org 75
## 21 Ensembl 54 May 2009 https://may2009.archive.ensembl.org 54
## current_release
## 1
## 2 *
## 3
## 4
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## 6
## 7
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## 9
## 10
## 11
## 12
## 13
## 14
## 15
## 16
## 17
## 18
## 19
## 20
## 21dataset <- useDataset("mfascicularis_gene_ensembl", mart = ensembl)
ensembl_99 <- useMart(biomart = "ENSEMBL_MART_ENSEMBL", host = "https://jan2020.archive.ensembl.org" )
#listDatasets(ensembl_99)
#Use ensembl_99
dataset2 <- useDataset("mfascicularis_gene_ensembl", mart = ensembl_99)
# List available attributes
attributes <- listAttributes(dataset)
head(attributes)## name description page
## 1 ensembl_gene_id Gene stable ID feature_page
## 2 ensembl_gene_id_version Gene stable ID version feature_page
## 3 ensembl_transcript_id Transcript stable ID feature_page
## 4 ensembl_transcript_id_version Transcript stable ID version feature_page
## 5 ensembl_peptide_id Protein stable ID feature_page
## 6 ensembl_peptide_id_version Protein stable ID version feature_pageattributes1 <- c("ensembl_gene_id", "external_gene_name", "wikigene_name")
result1 <- getBM(attributes = attributes1,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Niu_paper)`,
mart = dataset)
# Query for ensembl_gene_id, hsapiens_homolog_ensembl_gene, and hsapiens_homolog_associated_gene_name
attributes2 <- c("ensembl_gene_id", "hsapiens_homolog_ensembl_gene", "hsapiens_homolog_associated_gene_name")
result2 <- getBM(attributes = attributes2,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Niu_paper)`,
mart = dataset)
result3 <- getBM(attributes = attributes1,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Niu_paper)`,
mart = dataset2)
# Query Macaca fascicularis genes and their human homologs
result4 <- getBM(attributes = attributes2,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Niu_paper)`,
mart = dataset2)
# Merge result1 and result2
merged_result <- merge(result1, result2, by = "ensembl_gene_id", all = TRUE)
merged_result <- merge(merged_result, result3, by = "ensembl_gene_id", all = TRUE)
merged_result <- merge(merged_result, result4, by = "ensembl_gene_id", all = TRUE)
# Define a function to assign gene names based on column priority
assign_gene_name <- function(row) {
# Priority order of columns
priority_columns <- c("external_gene_name.x",
"hsapiens_homolog_associated_gene_name.x",
"external_gene_name.y",
"hsapiens_homolog_associated_gene_name.y")
# Iterate over the columns and return the first non-NA value
for (col in priority_columns) {
if (!is.na(row[[col]]) && row[[col]] != "") {
return(row[[col]])
}
}
return(NA) # Return NA if all values are missing or empty
}
# Apply the function to each row in the data frame
merged_result$assigned_gene_name <- apply(merged_result, 1, assign_gene_name)
# Remove duplicates based on 'ensembl_gene_id', keeping the first occurrence
merged_result <- merged_result[!duplicated(merged_result$ensembl_gene_id), ]
merged_result <- merged_result[,c("ensembl_gene_id","assigned_gene_name")]
merged_result <- merged_result[match(genes_2$`rownames(raw_list$Niu_paper)`,merged_result$ensembl_gene_id ),]
genes_2$genename <- merged_result$assigned_gene_name
genes.rest$genename <- genes.rest$`rownames(raw_list$Niu_paper)`
gene.all <- rbind(genes_2,genes.rest )
#check again
any(duplicated(gene.all$genename )) ## [1] TRUE# Step 1: Identify all duplicated gene names
duplicated_indices <- duplicated(gene.all$genename) | duplicated(gene.all$genename, fromLast = TRUE)
# Step 2: Replace all occurrences of duplicated gene names with corresponding row names
gene.all$genename[duplicated_indices] <- gene.all$`rownames(raw_list$Niu_paper)`[duplicated_indices]
# Step 3: Check for remaining duplicates
any(duplicated(gene.all$genename))## [1] FALSEgene.all <- gene.all[match(rownames(raw_list$Niu_paper),gene.all$`rownames(raw_list$Niu_paper)` ),]
##check again
rownames(raw_list$Niu_paper)[1:5]## [1] "ENSMFAG00000000001" "ENSMFAG00000000002" "ENSMFAG00000000003"
## [4] "ENSMFAG00000000004" "ENSMFAG00000000005"rownames(raw_list$Niu_paper) <-gene.all$genename
########################
raw_list$Niu_paper$embryo <- raw_list$Niu_paper$orig.ident
raw_list$Niu_paper$orig.ident <- "Niu"##pre-analysis
VlnPlot(raw_list$Niu_paper, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
Niu <-subset(raw_list$Niu_paper)
Niu <- NormalizeData(Niu, normalization.method = "LogNormalize", scale.factor = 10000)## Normalizing layer: countsNiu <- FindVariableFeatures(Niu, selection.method = "vst", nfeatures = 2000)## Finding variable features for layer counts# Identify the 10 most highly variable genes
head(VariableFeatures(Niu), 10)## [1] "HTRA4" "LAIR2" "ACTA2" "PRG2" "ISM2" "C15orf48"
## [7] "MMP2" "ECM1" "KHDC3L" "ESAM"##PCA
Niu <- ScaleData(Niu)## Centering and scaling data matrixNiu <- RunPCA(Niu, features = VariableFeatures(object = Niu))## PC_ 1
## Positive: GCM1, CYP11A1, ENSMFAG00000038879, ENSMFAG00000032513, ENSMFAG00000040054, S100P, IL1RN, SERPINB9, CTSL, GATA3
## CDKN1C, ATG4A, ENSMFAG00000041983, PECAM1, C11orf91, WIPI1, PTGIS, ENSMFAG00000035592, TINAGL1, CD55
## PHLDA2, ECM1, HMOX1, TFAP2A, CD164, VSIR, INSL4, GATA2, VAMP8, PCSK5
## Negative: MDK, PDXP, SNRPN, NME4, APOE, VCAN, 5-8S-rRNA, ENSMFAG00000030038, IFITM1, CCND2
## ENSMFAG00000000002, TERF1, CTHRC1, SFRP1, DPPA5, MAP1B, ENSMFAG00000030465, COL1A1, TIMP1, HGF
## ENSMFAG00000026088, ENSMFAG00000009591, COL3A1, HAS2, ALLC, TMEM88, RARRES2, POU5F1B, COL1A2, EGR1
## PC_ 2
## Positive: ENSMFAG00000037868, NLRP7, NHERF1, DPPA3, NLRP2, ALPL, CCKBR, ENSMFAG00000045681, KHDC1L, PDZK1
## ENSMFAG00000030465, TTR, GDF3, COL9A2, F2, S100A16, IHH, DNAH2, SLC7A8, LRP2
## ENSMFAG00000026088, ENSMFAG00000046163, SLC5A6, CDH2, SPINK1, FOLR1, TCN2, RRM1, NODAL, DPP4
## Negative: IGFBP3, ANXA1, COL1A1, COL3A1, DCN, COL1A2, TIMP1, PLOD2, TAGLN, ARHGDIB
## TGFBI, HAPLN1, SERPING1, COL6A3, HGF, PCOLCE, LUM, CD47, COL5A2, MMP2
## COLEC10, TPM2, SERPINE1, NID1, B2M, SULT1C4, COL6A2, CCDC80, PMP22, FBN1
## PC_ 3
## Positive: ENSMFAG00000000002, ENSMFAG00000009591, 5-8S-rRNA, GATA2, DPPA5, POU5F1B, ENSMFAG00000041983, 5-8S-rRNA.2, NMRK2, NFE2L3
## TFAP2C, FYB1, TUBA4A, ENSMFAG00000041377, AC087632.2, S100P, STARD10, VGLL1, DLX5, KHDC3L
## TERF1, SLTM, TFEB, ENO3, ENSMFAG00000032669, ENSMFAG00000002580, GPR78, AVPI1, PLEKHA6, SLC7A3
## Negative: FN1, GSN, TFPI, VTN, SERPINE2, PLTP, MAGED2, VCAN, CDH2, PDZK1
## ENSMFAG00000045681, F2, ENSMFAG00000046163, DNAH2, ITM2C, GPC3, RNASE4, TTR, SPARC, ENPP1
## TPM1, ECE1, IHH, TCN2, CST3, ENSMFAG00000044722, BMP2, PPFIBP2, COL9A2, DPPA3
## PC_ 4
## Positive: DCN, COL3A1, COL1A2, COL1A1, DAB2, PCOLCE, TGFBI, GADD45G, SPARC, HGF
## LUM, COL5A2, COL6A3, PDLIM2, TIMP1, ALLC, COLEC10, ENSMFAG00000025044, HSPB8, SULT1C4
## COL6A2, HAPLN1, OLFML3, EGFLAM, CTHRC1, CAVIN3, PDXP, POSTN, A2M, NID2
## Negative: ISM2, HTRA4, ESAM, LAIR2, PLAC8, NOTUM, PRG2, MFAP5, KCNJ15, NCAM1
## INSL4, CSF2RA, MCAM, MGLL, ENSMFAG00000016007, ADGRG6, PECAM1, MMP12, RASSF6, HS3ST1
## ICAM1, TINAGL1, LAMA4, LAIR1, SEPTIN3, C15orf48, JAM2, LVRN, ECM1, LGALS3
## PC_ 5
## Positive: ENSMFAG00000031396, ENSMFAG00000045627, ENSMFAG00000002784, ENSMFAG00000003363, IQCD, ENSMFAG00000034698, SAT1, ENSMFAG00000038834, PPP1R14A, ASB2
## ENSMFAG00000046277, ESRRG, ENSMFAG00000024252, ENSMFAG00000002580, PRR9, SLC13A4, HOPX, ENSMFAG00000032669, CGA, ENSMFAG00000046378
## HSPB8, WNT10B, MFSD2A, SCG3, CCR7, RASA1, ENSMFAG00000025044, GPR146, KRT23, IL1RN
## Negative: SLC30A2, ATP6V0A4, ATP12A, CLPS, ANXA8, ENSMFAG00000033208, S100A14, CD53, SLC12A3, PLD2
## CDX1, SMIM1, ATP6V1B1, LRP2, AC034228.3, STS, FFAR4, S100A6, SLC34A2, GM2A
## SUSD2, A4GALT, KANK4, MET, SH3BGRL2, EMP2, LAMA1, GPRC5A, RAB11FIP4, PTGESVizDimLoadings(Niu, dims = 1:2, reduction = "pca")
DimPlot(Niu, reduction = "pca") + NoLegend()
DimHeatmap(Niu, dims = 1:6, cells = 500, balanced = TRUE)
ElbowPlot(Niu)
library(clustree)
Niu <- FindNeighbors(Niu, dims = 1:10, k=10) ## Computing nearest neighbor graph## Computing SNNNiu <- FindClusters(Niu, resolution = seq(0.1,0.6,0.1), verbose = FALSE)
clustree(Niu)
Niu <- FindClusters(Niu, resolution = 0.5)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 600
## Number of edges: 7321
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8990
## Number of communities: 11
## Elapsed time: 0 secondsNiu <- RunUMAP(Niu, dims = 1:10)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 20:04:04 UMAP embedding parameters a = 0.9922 b = 1.112## 20:04:04 Read 600 rows and found 10 numeric columns## 20:04:04 Using Annoy for neighbor search, n_neighbors = 30## 20:04:04 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 20:04:04 Writing NN index file to temp file C:\Users\fxyin\AppData\Local\Temp\Rtmpi6jUzC\filee18421f4d1b
## 20:04:04 Searching Annoy index using 1 thread, search_k = 3000
## 20:04:04 Annoy recall = 100%
## 20:04:05 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 20:04:06 Initializing from normalized Laplacian + noise (using RSpectra)
## 20:04:06 Commencing optimization for 500 epochs, with 22016 positive edges
## 20:04:09 Optimization finished# individual clusters
DimPlot(Niu, reduction = "umap")
DimPlot(Niu, reduction = "umap", group.by = "stage")
##################use genemodules#############
epi <- list(c("POU5F1","DPPA5", "KHDC3L", "FOXH1"))
TE <- list(c("CGA", "XAGE3", "PGF", "KRT7", "GATA2" ))
PE <- list(c("TTR", "FOXA2", "APOA1", "DPPA3", "SPINK1"))
EXMC <- list(c("COL3A1", "POSTN", "HAND2", "COL1A1"))
Niu <- AddModuleScore(Niu,
features = epi,
name="epi_enriched", random.seed = 1)## Warning: The following features are not present in the object: POU5F1, not
## searching for symbol synonymsNiu <- AddModuleScore(Niu,
features = TE,
name="TE_enriched", random.seed = 1)
Niu <- AddModuleScore(Niu,
features = PE,
name="PE_enriched", random.seed = 1)## Warning: The following features are not present in the object: APOA1, not
## searching for symbol synonymsNiu <- AddModuleScore(Niu,
features = EXMC,
name="EXMC_enriched", random.seed = 1)
# Plot scores
DimPlot(Niu, reduction = "umap")
FeaturePlot(Niu,
features = "epi_enriched1", label = TRUE, repel = TRUE) +
scale_color_distiller(palette = "RdYlBu")## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
FeaturePlot(Niu,
features = "TE_enriched1", label = TRUE, repel = TRUE) +
scale_color_distiller(palette = "RdYlBu")## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
FeaturePlot(Niu,
features = "PE_enriched1", label = TRUE, repel = TRUE) +
scale_color_distiller(palette = "RdYlBu")## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
FeaturePlot(Niu,
features = "EXMC_enriched1", label = TRUE, repel = TRUE) +
scale_color_distiller(palette = "RdYlBu")## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
####rename the clusters#########################
Niu$anno <- c("NA")
Niu$anno[which(Idents(Niu) %in% c('4', '6'))] <- c("PE")
Niu$anno[which(Idents(Niu) %in% c('1','2', '9'))] <- c("Epi")
Niu$anno[which(Idents(Niu) %in% c('0', '7'))] <- c("EXMC")
Niu$anno[which(Idents(Niu) %in% c('3','8', '10'))] <- c("TE")
DimPlot(Niu, reduction = "umap", group.by = "anno")
###add the anno
saveRDS(Niu, file = "Monkey_Niu_reanno.rds")
raw_list$Niu_paper$anno <- Niu$anno
#####plots########
VlnPlot(raw_list$Niu_paper, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
meta <- raw_list$Niu_paper@meta.data
df2 <- as.data.frame(table(meta$stage, meta$anno))
##plot###n_cells before QC
ggplot(data=df2, aes(x=Var1, y=Freq, fill=Var2)) +
geom_bar(stat="identity")+
theme(text=element_text(size=30))
##Yang paper ##fix gene names
genes <- as.data.frame(rownames(raw_list$Yang))
genes_2 <- genes[grep("ENSMFAG", genes[,1]),,drop=FALSE]
genes.rest <- genes[-grep("ENSMFAG", genes[,1]),,drop=FALSE]
######use both mfascicularis and hsapiens biomart#######
attributes1 <- c("ensembl_gene_id", "external_gene_name", "wikigene_name")
result1 <- getBM(attributes = attributes1,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Yang)`,
mart = dataset)
# Query for ensembl_gene_id, hsapiens_homolog_ensembl_gene, and hsapiens_homolog_associated_gene_name
attributes2 <- c("ensembl_gene_id", "hsapiens_homolog_ensembl_gene", "hsapiens_homolog_associated_gene_name")
result2 <- getBM(attributes = attributes2,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Yang)`,
mart = dataset)
result3 <- getBM(attributes = attributes1,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Yang)`,
mart = dataset2)
# Query Macaca fascicularis genes and their human homologs
result4 <- getBM(attributes = attributes2,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Yang)`,
mart = dataset2)
# Merge result1 and result2
merged_result <- merge(result1, result2, by = "ensembl_gene_id", all = TRUE)
merged_result <- merge(merged_result, result3, by = "ensembl_gene_id", all = TRUE)
merged_result <- merge(merged_result, result4, by = "ensembl_gene_id", all = TRUE)
# Define a function to assign gene names based on column priority
assign_gene_name <- function(row) {
# Priority order of columns
priority_columns <- c("external_gene_name.x",
"hsapiens_homolog_associated_gene_name.x",
"external_gene_name.y",
"hsapiens_homolog_associated_gene_name.y")
# Iterate over the columns and return the first non-NA value
for (col in priority_columns) {
if (!is.na(row[[col]]) && row[[col]] != "") {
return(row[[col]])
}
}
return(NA) # Return NA if all values are missing or empty
}
# Apply the function to each row in the data frame
merged_result$assigned_gene_name <- apply(merged_result, 1, assign_gene_name)
# Remove duplicates based on 'ensembl_gene_id', keeping the first occurrence
merged_result <- merged_result[!duplicated(merged_result$ensembl_gene_id), ]
merged_result <- merged_result[,c("ensembl_gene_id","assigned_gene_name")]
merged_result <- merged_result[match(genes_2$`rownames(raw_list$Yang)`,merged_result$ensembl_gene_id ),]
genes_2$genename <- merged_result$assigned_gene_name
genes.rest$genename <- genes.rest$`rownames(raw_list$Yang)`
gene.all <- rbind(genes_2,genes.rest )
#check again
any(duplicated(gene.all$genename )) ## [1] TRUE# Step 1: Identify all duplicated gene names
duplicated_indices <- duplicated(gene.all$genename) | duplicated(gene.all$genename, fromLast = TRUE)
# Step 2: Replace all occurrences of duplicated gene names with corresponding row names
gene.all$genename[duplicated_indices] <- gene.all$`rownames(raw_list$Yang)`[duplicated_indices]
# Step 3: Check for remaining duplicates
any(duplicated(gene.all$genename))## [1] FALSEgene.all <- gene.all[match(rownames(raw_list$Yang),gene.all$`rownames(raw_list$Yang)` ),]
##check again
rownames(raw_list$Yang)[1:5]## [1] "PGBD2" "ZNF692" "ZNF672" "SH3BP5L" "LYPD8"rownames(raw_list$Yang) <-gene.all$genename##Yang paper
##no annotation
Yang <- raw_list$Yang
VlnPlot(Yang, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
Yang <- NormalizeData(Yang, normalization.method = "LogNormalize", scale.factor = 10000)## Normalizing layer: countsYang <- FindVariableFeatures(Yang, selection.method = "vst", nfeatures = 2000)## Finding variable features for layer counts# Identify the 10 most highly variable genes
head(VariableFeatures(Yang), 10)## [1] "APOA1" "PRG2" "ENSMFAG00000002889"
## [4] "FGF18" "MMP12" "TTR"
## [7] "HOPX" "SLC40A1" "PTPN21"
## [10] "LGALS16"##PCA
Yang <- ScaleData(Yang)## Centering and scaling data matrixYang <- RunPCA(Yang, features = VariableFeatures(object = Yang))## PC_ 1
## Positive: ENSMFAG00000040595, ENSMFAG00000036985, SNRPN, PTPRG, TCF4, PRKD1, GPC6, PBX1, TNNT1, ADD3
## EXT1, LSAMP, DPPA4, IGF1R, ENSMFAG00000002899, CADM1, BAMBI, DPYSL3, PTPRD, IFITM1
## PDPN, POU5F1B, ZNF462, FUOM, VIM, APOE, CXADR, DPPA5, FOXH1, IGF2BP2
## Negative: CDKN1C, PGF, CGA, HSD3B1, REEP5, GATA2, GCM1, CGB2, CD164, CYP11A1
## TFAP2A, GATA3, FDX1, PHLDA2, STARD10, BACE2, CGB8, VAMP8, PTGIS, PAPOLA
## CSGALNACT1, XAGE3, ST8SIA4, PID1, MLLT1, CD55, ARID3A, AVPI1, GPR137, BHLHE40
## PC_ 2
## Positive: ENSMFAG00000037514, ISM2, LGALS3, ESAM, ECM1, INSL4, HSD17B2, NCAM1, TINAGL1, JPT1
## PLOD2, SERPING1, TGFB2, SERPINE1, MGLL, RASSF6, SRI, FBN1, ENSMFAG00000004652, LAIR2
## ITM2B, PLAC8, HS3ST1, C15orf48, TMEM132A, TPM1, ARHGDIB, CDH5, CORO1A, ITGA1
## Negative: FSCN1, RASAL2, RAB11FIP1, TBX3, SLC40A1, KLHL7, PPM1B, RAB8B, FGF18, PTPN21
## GADD45G, LGALS16, FGD6, F2RL1, HSPB8, SLC19A1, WNK2, AGAP1, LITAF, NEURL1
## CBLB, ZNF728, ID2, APOE, MLLT1, OVOL1, NEDD4L, TEAD4, ENSMFAG00000040354, ST8SIA4
## PC_ 3
## Positive: HGF, COL3A1, EGFLAM, COL6A3, PITX1, COL6A2, MEIS2, CCDC80, COL4A1, HMGA2
## COL4A2, COL5A2, ADAM12, ZEB2, TEK, RSPO2, MSRB3, PITX2, TMEM88, F5
## SULT1C4, CTHRC1, HAND2, ZFPM2, CREB3L1, COL6A1, TIMP1, ITIH5, NAV3, WNT2
## Negative: L1TD1, POU5F1B, KHDC3L, DPPA5, ENSMFAG00000037727, NANOG, IFITM1, OLFM2, PTPRZ1, ZNF208
## KCTD8, SEPHS1, ANOS1, ENSMFAG00000003315, DNMT3B, IGFBP2, GABRB3, TDGF1, OOEP, CXCL12
## ENO3, SFRP2, TUBB2B, RRAGD, ENSMFAG00000043424, OPCML, ZYG11A, IGFBP6, LRRIQ1, CMBL
## PC_ 4
## Positive: ENSMFAG00000038147, LRP2, NR2F2, CLMP, ANXA8, ADGRL3, PCLAF, KHDRBS3, NRP1, ATP6V1C2
## LRPAP1, BCAM, MET, SDK1, KCNMA1, TMC5, MED12L, LAMA1, ATG10, S100A10
## RPS6KA4, CCKBR, LYN, PRXL2A, ATP12A, NPM1, KIAA1217, MSX2, XAGE3, TSPAN5
## Negative: ENPP1, JAM2, PRG2, SERPINE2, ETS2, LAIR2, QSOX1, CHST11, MMP12, C15orf48
## SAT1, LAIR1, MMP2, ESAM, ADGRG6, SERPINE1, ENTPD1, IER3, PECAM1, RTN2
## SH3BP4, PPM1N, CALCB, VGLL3, PLPP3, EDNRB, HS3ST1, ITGA2, HLA-E, SEPTIN3
## PC_ 5
## Positive: APOA1, S100A16, ENSMFAG00000045681, FTH1, S100A6, LRP2, S100A14, IHH, PRXL2A, APBB2
## FOXA2, KANK4, ENSMFAG00000045359, WWC2, SERPINE2, SOX17, NRG1, ADAM19, LAMA1, SLC9A3R1
## SMIM1, F2, CDA, LGR5, APOC3, TM4SF5, TNNC1, HNF4A, LLGL2, SYNE2
## Negative: HOPX, CGB7, ETV4, HSPB1, CGB8, ASB2, DPPA4, RBM24, SDC2, KRT23
## CGB2, SNRPN, LHB, PDLIM2, CGA, SBSN, NOSIP, SLC2A1, VSIG1, LSAMP
## ENSMFAG00000037727, POU5F1B, PLCB1, PTPRZ1, CKMT1B, ENSMFAG00000024252, ATF3, NANOG, HEY1, EPS8L1VizDimLoadings(Yang, dims = 1:2, reduction = "pca")
DimPlot(Yang, reduction = "pca") + NoLegend()
DimHeatmap(Yang, dims = 1:6, cells = 500, balanced = TRUE)
ElbowPlot(Yang)
library(clustree)
Yang <- FindNeighbors(Yang, dims = 1:10, k=10) ## Computing nearest neighbor graph## Computing SNNYang <- FindClusters(Yang, resolution = seq(0.1,0.6,0.1), verbose = FALSE)
clustree(Yang)
Yang <- FindClusters(Yang, resolution = 0.5)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 15503
## Number of edges: 204772
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9475
## Number of communities: 23
## Elapsed time: 0 secondsYang <- RunUMAP(Yang, dims = 1:10)## 20:05:08 UMAP embedding parameters a = 0.9922 b = 1.112## 20:05:08 Read 15503 rows and found 10 numeric columns## 20:05:08 Using Annoy for neighbor search, n_neighbors = 30## 20:05:08 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 20:05:10 Writing NN index file to temp file C:\Users\fxyin\AppData\Local\Temp\Rtmpi6jUzC\filee18788c7b8
## 20:05:10 Searching Annoy index using 1 thread, search_k = 3000
## 20:05:16 Annoy recall = 100%
## 20:05:17 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 20:05:19 Initializing from normalized Laplacian + noise (using RSpectra)
## 20:05:21 Commencing optimization for 200 epochs, with 614276 positive edges
## 20:05:44 Optimization finished# individual clusters
DimPlot(Yang, reduction = "umap")
DimPlot(Yang, reduction = "umap", group.by = "stage")
Yang <- AddModuleScore(Yang,
features = epi,
name="epi_enriched", random.seed = 1)## Warning: The following features are not present in the object: POU5F1, not
## searching for symbol synonymsYang <- AddModuleScore(Yang,
features = TE,
name="TE_enriched", random.seed = 1)
Yang <- AddModuleScore(Yang,
features = PE,
name="PE_enriched", random.seed = 1)
Yang <- AddModuleScore(Yang,
features = EXMC,
name="EXMC_enriched", random.seed = 1)
# Plot scores
DimPlot(Yang, reduction = "umap")
FeaturePlot(Yang,
features = "epi_enriched1", label = TRUE, repel = TRUE) +
scale_color_distiller(palette = "RdYlBu")## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
FeaturePlot(Yang,
features = "TE_enriched1", label = TRUE, repel = TRUE) +
scale_color_distiller(palette = "RdYlBu")## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
FeaturePlot(Yang,
features = "PE_enriched1", label = TRUE, repel = TRUE) +
scale_color_distiller(palette = "RdYlBu")## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
FeaturePlot(Yang,
features = "EXMC_enriched1", label = TRUE, repel = TRUE) +
scale_color_distiller(palette = "RdYlBu")## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
####rename the clusters#########################
Yang$anno <- c("NA")
Yang$anno[which(Idents(Yang) %in% c('18'))] <- c("PE")
Yang$anno[which(Idents(Yang) %in% c('2', '19', '22'))] <- c("EXMC")
Yang$anno[which(Idents(Yang) %in% c('1','9','17', '20','21'))] <- c("Epi")
Yang$anno[which(Yang$anno=="NA")] <- c("TE")
DimPlot(Yang, reduction = "umap", group.by = "anno")
###add the anno
saveRDS(Yang, file = "Monkey_Yang_reanno.rds")
raw_list$Yang$anno <- Yang$anno
######plot####################
VlnPlot(raw_list$Yang, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
meta <- raw_list$Yang@meta.data
df2 <- as.data.frame(table(meta$stage, meta$anno))
##plot###n_cells before QC
ggplot(data=df2, aes(x=Var1, y=Freq, fill=Var2)) +
geom_bar(stat="identity")+
theme(text=element_text(size=30))
##Zhai, 2022 dataset ##fix gene names
genes <- as.data.frame(rownames(raw_list$Zhai_2022))
genes_2 <- genes[grep("ENSMFAG", genes[,1]),,drop=FALSE]
genes.rest <- genes[-grep("ENSMFAG", genes[,1]),,drop=FALSE]
######use both mfascicularis and hsapiens biomart#######
attributes1 <- c("ensembl_gene_id", "external_gene_name", "wikigene_name")
result1 <- getBM(attributes = attributes1,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Zhai_2022)`,
mart = dataset)
# Query for ensembl_gene_id, hsapiens_homolog_ensembl_gene, and hsapiens_homolog_associated_gene_name
attributes2 <- c("ensembl_gene_id", "hsapiens_homolog_ensembl_gene", "hsapiens_homolog_associated_gene_name")
result2 <- getBM(attributes = attributes2,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Zhai_2022)`,
mart = dataset)
result3 <- getBM(attributes = attributes1,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Zhai_2022)`,
mart = dataset2)
# Query Macaca fascicularis genes and their human homologs
result4 <- getBM(attributes = attributes2,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Zhai_2022)`,
mart = dataset2)
# Merge result1 and result2
merged_result <- merge(result1, result2, by = "ensembl_gene_id", all = TRUE)
merged_result <- merge(merged_result, result3, by = "ensembl_gene_id", all = TRUE)
merged_result <- merge(merged_result, result4, by = "ensembl_gene_id", all = TRUE)
# Define a function to assign gene names based on column priority
assign_gene_name <- function(row) {
# Priority order of columns
priority_columns <- c("external_gene_name.x",
"hsapiens_homolog_associated_gene_name.x",
"external_gene_name.y",
"hsapiens_homolog_associated_gene_name.y")
# Iterate over the columns and return the first non-NA value
for (col in priority_columns) {
if (!is.na(row[[col]]) && row[[col]] != "") {
return(row[[col]])
}
}
return(NA) # Return NA if all values are missing or empty
}
# Apply the function to each row in the data frame
merged_result$assigned_gene_name <- apply(merged_result, 1, assign_gene_name)
# Remove duplicates based on 'ensembl_gene_id', keeping the first occurrence
merged_result <- merged_result[!duplicated(merged_result$ensembl_gene_id), ]
merged_result <- merged_result[,c("ensembl_gene_id","assigned_gene_name")]
merged_result <- merged_result[match(genes_2$`rownames(raw_list$Zhai_2022)`,merged_result$ensembl_gene_id ),]
genes_2$genename <- merged_result$assigned_gene_name
genes.rest$genename <- genes.rest$`rownames(raw_list$Zhai_2022)`
gene.all <- rbind(genes_2,genes.rest )
#check again
any(duplicated(gene.all$genename )) ## [1] TRUE# Step 1: Identify all duplicated gene names
duplicated_indices <- duplicated(gene.all$genename) | duplicated(gene.all$genename, fromLast = TRUE)
# Step 2: Replace all occurrences of duplicated gene names with corresponding row names
gene.all$genename[duplicated_indices] <- gene.all$`rownames(raw_list$Zhai_2022)`[duplicated_indices]
# Step 3: Check for remaining duplicates
any(duplicated(gene.all$genename))## [1] FALSEgene.all <- gene.all[match(rownames(raw_list$Zhai_2022),gene.all$`rownames(raw_list$Zhai_2022)` ),]
##check again
rownames(raw_list$Zhai_2022)[1:5]## [1] "PGBD2" "ZNF692" "ZNF672" "SH3BP5L" "LYPD8"rownames(raw_list$Zhai_2022) <-gene.all$genenamezhai_2022_anno <- read.csv("D:/xueying-work/afterPHD/Liu-lab/project1-embryo benchmarking/2024April/data/monkey-Zhai,2019/MFE56636-meta.csv", header = TRUE, sep = ",")
##check colnames
colnames(raw_list$Zhai_2022)[1:5]## [1] "AAACCCAAGACGGTCA-1_1_1_1" "AAACCCACAGTAACAA-1_1_1_1"
## [3] "AAACCCAGTACTCAAC-1_1_1_1" "AAACCCAGTATAGGAT-1_1_1_1"
## [5] "AAACCCAGTTCCCACT-1_1_1_1"library(stringr)
df <- as.data.frame(str_split_fixed( zhai_2022_anno$X, "_", 2))
df$V3 <- as.data.frame(str_split_fixed( df$V2, "-", 2))[2]
table(df$V1, df$V3$V2)##
## 1 2 3 4 5 6 7
## E20-E1 3559 0 0 0 0 0 0
## E20-E2 0 8070 0 0 0 0 0
## E23-E1 0 0 9073 0 0 0 0
## E23-E2 0 0 0 8752 0 0 0
## E26-E1 0 0 0 0 9726 0 0
## E26-Y1 0 0 0 0 0 5318 0
## E29-E1 0 0 0 0 0 0 12138cells <- raw_list$Zhai_2022@meta.data[,c(1,4,7)]
cells$id <- rownames(cells)
cells$code <- as.data.frame(str_split_fixed(cells$id, "-", 2))[1]
#check
unique(cells$stage)## [1] "CS8-e1" "CS8-e2" "CS9-e1" "CS9-e2" "CS11-e1" "CS11-ys1" "CS11-e2"cells$code2 <- "NA"
cells$code2[which(cells$stage=="CS8-e1")] <- paste0(cells$code$V1, "-1")[which(cells$stage=="CS8-e1")]
cells$code2[which(cells$stage=="CS8-e2")] <- paste0(cells$code$V1, "-2")[which(cells$stage=="CS8-e2")]
cells$code2[which(cells$stage=="CS9-e1")] <- paste0(cells$code$V1, "-3")[which(cells$stage=="CS9-e1")]
cells$code2[which(cells$stage=="CS9-e2")] <- paste0(cells$code$V1, "-4")[which(cells$stage=="CS9-e2")]
cells$code2[which(cells$stage=="CS11-e1")] <- paste0(cells$code$V1, "-5")[which(cells$stage=="CS11-e1")]
cells$code2[which(cells$stage=="CS11-ys1")] <- paste0(cells$code$V1, "-6")[which(cells$stage=="CS11-ys1")]
cells$code2[which(cells$stage=="CS11-e2")] <- paste0(cells$code$V1, "-7")[which(cells$stage=="CS11-e2")]
length(which(df$V2 %in% cells$code2))## [1] 55335#check which cells were not found
df2 <- df[-which(df$V2 %in% cells$code2),]
##match cell annotations
df2 <- zhai_2022_anno[,c(1,4,5,8)]
df2$code2 <- as.data.frame(str_split_fixed( zhai_2022_anno$X, "_", 2))[,2]
cells <- cells[,c(1,2,6)]
colnames(cells) <- c("dataset", "group", "code2")
meta <- merge(cells, df2, by="code2",all=TRUE )
meta <- meta[which(cells$code2 %in% meta$code2),]
meta <- meta[match(cells$code2, meta$code2),]
meta$cell_type[which(is.na(meta$cell_type))] <- "NA"
raw_list$Zhai_2022$anno <- meta$cell_type
#########plot###################
VlnPlot(raw_list$Zhai_2022, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
meta <- raw_list$Zhai_2022@meta.data
df2 <- as.data.frame(table(meta$stage, meta$anno))
##plot###n_cells before QC
ggplot(data=df2, aes(x=Var1, y=Freq, fill=Var2)) +
geom_bar(stat="identity")+
theme(text=element_text(size=30))
##Gong dataset
genes <- as.data.frame(rownames(raw_list$Gong))
genes_2 <- genes[grep("ENSMFAG", genes[,1]),,drop=FALSE]
genes.rest <- genes[-grep("ENSMFAG", genes[,1]),,drop=FALSE]
######use both mfascicularis and hsapiens biomart#######
attributes1 <- c("ensembl_gene_id", "external_gene_name", "wikigene_name")
result1 <- getBM(attributes = attributes1,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Gong)`,
mart = dataset)
# Query for ensembl_gene_id, hsapiens_homolog_ensembl_gene, and hsapiens_homolog_associated_gene_name
attributes2 <- c("ensembl_gene_id", "hsapiens_homolog_ensembl_gene", "hsapiens_homolog_associated_gene_name")
result2 <- getBM(attributes = attributes2,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Gong)`,
mart = dataset)
result3 <- getBM(attributes = attributes1,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Gong)`,
mart = dataset2)
# Query Macaca fascicularis genes and their human homologs
result4 <- getBM(attributes = attributes2,
filters = "ensembl_gene_id",
values = genes_2$`rownames(raw_list$Gong)`,
mart = dataset2)
# Merge result1 and result2
merged_result <- merge(result1, result2, by = "ensembl_gene_id", all = TRUE)
merged_result <- merge(merged_result, result3, by = "ensembl_gene_id", all = TRUE)
merged_result <- merge(merged_result, result4, by = "ensembl_gene_id", all = TRUE)
# Define a function to assign gene names based on column priority
assign_gene_name <- function(row) {
# Priority order of columns
priority_columns <- c("external_gene_name.x",
"hsapiens_homolog_associated_gene_name.x",
"external_gene_name.y",
"hsapiens_homolog_associated_gene_name.y")
# Iterate over the columns and return the first non-NA value
for (col in priority_columns) {
if (!is.na(row[[col]]) && row[[col]] != "") {
return(row[[col]])
}
}
return(NA) # Return NA if all values are missing or empty
}
# Apply the function to each row in the data frame
merged_result$assigned_gene_name <- apply(merged_result, 1, assign_gene_name)
# Remove duplicates based on 'ensembl_gene_id', keeping the first occurrence
merged_result <- merged_result[!duplicated(merged_result$ensembl_gene_id), ]
merged_result <- merged_result[,c("ensembl_gene_id","assigned_gene_name")]
merged_result <- merged_result[match(genes_2$`rownames(raw_list$Gong)`,merged_result$ensembl_gene_id ),]
genes_2$genename <- merged_result$assigned_gene_name
genes.rest$genename <- genes.rest$`rownames(raw_list$Gong)`
gene.all <- rbind(genes_2,genes.rest )
#check again
any(duplicated(gene.all$genename )) ## [1] TRUE# Step 1: Identify all duplicated gene names
duplicated_indices <- duplicated(gene.all$genename) | duplicated(gene.all$genename, fromLast = TRUE)
# Step 2: Replace all occurrences of duplicated gene names with corresponding row names
gene.all$genename[duplicated_indices] <- gene.all$`rownames(raw_list$Gong)`[duplicated_indices]
# Step 3: Check for remaining duplicates
any(duplicated(gene.all$genename))## [1] FALSEgene.all <- gene.all[match(rownames(raw_list$Gong),gene.all$`rownames(raw_list$Gong)`),]
##check again
rownames(raw_list$Gong)[1:5]## [1] "PGBD2" "ZNF692" "ZNF672" "SH3BP5L" "LYPD8"rownames(raw_list$Gong) <-gene.all$genename##Gong dataset
#load R workspace
workspace_file <- "D:/xueying-work/afterPHD/Liu-lab/project1-embryo benchmarking/2024April/data/Monkey-Gong/Figure2_Allcells.Rdata"
# Load the workspace
load(workspace_file)
colnames(Vitro)[1:5]## [1] "AAACCCAAGGCTGAAC-1_1" "AAACCCATCGGAGTAG-1_1" "AAACCCATCGTGCGAC-1_1"
## [4] "AAACGAAGTCCACAGC-1_1" "AAACGCTGTTGTGGAG-1_1"df <- Vitro@meta.data
df2 <- raw_list$Gong@meta.data
df2$id <- rownames(df2)
y1 <- as.data.frame(str_split_fixed(rownames(df), "_", 4))
unique(y1$V2)## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "17" "18"unique(y1$V3)## [1] "" "11" "12" "13" "14" "15" "16" "17" "18" "19" "20" "23" "24"unique(y1$V4)## [1] "" "21" "22"unique(df$group)## [1] "ME20b" "ME22b1" "ME22b2" "ME23b1" "ME23b2" "ME23b3" "ME24b1"
## [8] "ME24b2" "ME25b1" "ME25b2" "ME18" "ME19" "ME20" "ME21"
## [15] "ME22" "ME23" "ME24" "ME25" "ME18b_1" "ME18b_2" "ME19b_1"
## [22] "ME19b_2" "ME18C_1" "ME21C_1"y1$id <- y1$V2
y1$id[which(y1$V3!="")] <- y1$V3[which(y1$V3!="")]
y1$id[which(y1$V4!="")] <- y1$V4[which(y1$V4!="")]
y1$group <- df$group
y1$cluster <- df$Cluster
table(y1$id, y1$group)##
## ME18 ME18b_1 ME18b_2 ME18C_1 ME19 ME19b_1 ME19b_2 ME20 ME20b ME21 ME21C_1
## 1 0 0 0 0 0 0 0 0 2443 0 0
## 10 0 0 0 0 0 0 0 0 0 0 0
## 11 3759 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 3769 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 3979 0 0 0
## 14 0 0 0 0 0 0 0 0 0 1453 0
## 15 0 0 0 0 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 0 0 0 0
## 18 0 0 0 0 0 0 0 0 0 0 0
## 19 0 1211 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0
## 20 0 0 874 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 2843 0 0 0 0 0
## 22 0 0 0 0 0 0 3990 0 0 0 0
## 23 0 0 0 899 0 0 0 0 0 0 0
## 24 0 0 0 0 0 0 0 0 0 0 2401
## 3 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0
## 7 0 0 0 0 0 0 0 0 0 0 0
## 8 0 0 0 0 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0 0 0 0 0
##
## ME22 ME22b1 ME22b2 ME23 ME23b1 ME23b2 ME23b3 ME24 ME24b1 ME24b2 ME25
## 1 0 0 0 0 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0 0 0 0 0
## 11 0 0 0 0 0 0 0 0 0 0 0
## 12 0 0 0 0 0 0 0 0 0 0 0
## 13 0 0 0 0 0 0 0 0 0 0 0
## 14 0 0 0 0 0 0 0 0 0 0 0
## 15 2503 0 0 0 0 0 0 0 0 0 0
## 16 0 0 0 2556 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0 5490 0 0 0
## 18 0 0 0 0 0 0 0 0 0 0 3209
## 19 0 0 0 0 0 0 0 0 0 0 0
## 2 0 1975 0 0 0 0 0 0 0 0 0
## 20 0 0 0 0 0 0 0 0 0 0 0
## 21 0 0 0 0 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0 0 0 0 0
## 23 0 0 0 0 0 0 0 0 0 0 0
## 24 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 1997 0 0 0 0 0 0 0 0
## 4 0 0 0 0 5591 0 0 0 0 0 0
## 5 0 0 0 0 0 6053 0 0 0 0 0
## 6 0 0 0 0 0 0 5459 0 0 0 0
## 7 0 0 0 0 0 0 0 0 3212 0 0
## 8 0 0 0 0 0 0 0 0 0 2185 0
## 9 0 0 0 0 0 0 0 0 0 0 0
##
## ME25b1 ME25b2
## 1 0 0
## 10 0 3311
## 11 0 0
## 12 0 0
## 13 0 0
## 14 0 0
## 15 0 0
## 16 0 0
## 17 0 0
## 18 0 0
## 19 0 0
## 2 0 0
## 20 0 0
## 21 0 0
## 22 0 0
## 23 0 0
## 24 0 0
## 3 0 0
## 4 0 0
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 3311 0any(duplicated(y1$V1))## [1] TRUEy2 <- as.data.frame(str_split_fixed(rownames(df2), "_", 2))
unique(y2$V2)## [1] "1_1_1_1_1_1_1_1_1_1_1_1" "2_1_1_1_1_1_1_1_1_1_1_1"
## [3] "1_1_1_1_1_1_1_1_1_1_1" "2_1_1_1_1_1_1_1_1_1_1"
## [5] "1_1_1_1_1_1_1_1_1_1" "2_1_1_1_1_1_1_1_1_1"
## [7] "1_1_1_1_1_1_1_1_1" "2_1_1_1_1_1_1_1_1"
## [9] "1_1_1_1_1_1_1_1" "2_1_1_1_1_1_1_1"
## [11] "1_1_1_1_1_1_1" "2_1_1_1_1_1_1"
## [13] "1_1_1_1_1_1" "2_1_1_1_1_1"
## [15] "1_1_1_1_1" "2_1_1_1_1"
## [17] "1_1_1_1" "2_1_1_1"
## [19] "1_1_1" "2_1_1"
## [21] "1_1" "2_1"
## [23] "1" "2"y2$group <- "NA"
y2$group[which(y2$V2=="1_1_1_1_1_1_1_1_1_1_1_1")] <- "ME18"
y2$group[which(y2$V2=="2_1_1_1_1_1_1_1_1_1_1_1")] <- "ME23b3"
y2$group[which(y2$V2=="1_1_1_1_1_1_1_1_1_1_1")] <- "ME23b2"
y2$group[which(y2$V2=="2_1_1_1_1_1_1_1_1_1_1")] <- "ME23b1"
y2$group[which(y2$V2=="1_1_1_1_1_1_1_1_1_1")] <- "ME23"
y2$group[which(y2$V2=="2_1_1_1_1_1_1_1_1_1")] <- "ME22b2"
y2$group[which(y2$V2=="1_1_1_1_1_1_1_1_1")] <- "ME22b1"
y2$group[which(y2$V2=="2_1_1_1_1_1_1_1_1")] <- "ME22"
y2$group[which(y2$V2=="1_1_1_1_1_1_1_1")] <- "ME21C_1"
y2$group[which(y2$V2=="2_1_1_1_1_1_1_1")] <- "ME21"
y2$group[which(y2$V2=="1_1_1_1_1_1_1")] <- "ME20b"
y2$group[which(y2$V2=="2_1_1_1_1_1_1")] <- "ME20"
y2$group[which(y2$V2=="1_1_1_1_1_1")] <- "ME19b_2"
y2$group[which(y2$V2=="2_1_1_1_1_1")] <- "ME19b_1"
y2$group[which(y2$V2=="1_1_1_1_1")] <- "ME19"
y2$group[which(y2$V2=="2_1_1_1_1")] <- "ME18C_1"
y2$group[which(y2$V2=="1_1_1_1")] <- "ME18b_2"
y2$group[which(y2$V2=="2_1_1_1")] <- "ME25b2"
y2$group[which(y2$V2=="1_1_1")] <- "ME25b1"
y2$group[which(y2$V2=="2_1_1")] <- "ME25"
y2$group[which(y2$V2=="1_1")] <- "ME24b2"
y2$group[which(y2$V2=="2_1")] <- "ME24b1"
y2$group[which(y2$V2=="1")] <- "ME24"
y2$group[which(y2$V2=="2")] <- "ME18b_1"
y2$id2 <- paste0(y2$V1,"_",y2$group)
y1$id2 <- paste0(y1$V1,"_",y1$group)
length(which(y2$id2 %in% y1$id2))## [1] 74466y2$rownames <- rownames(df2)
meta <- merge(y2, y1, by="id2", all=TRUE)
meta <- meta[which(meta$rownames %in% rownames(df2)),]
meta <- meta[,c(5,11,12)]
meta <- meta[match(colnames(raw_list$Gong) ,meta$rownames),]
meta$group.y[which(is.na(meta$group.y))] <- "NA"
meta$cluster[which(is.na(meta$cluster))] <- "NA"
raw_list$Gong$anno <- meta$cluster
raw_list$Gong$embryo <- meta$group.y
rm(Vitro)
###################################
VlnPlot(raw_list$Gong, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
meta <- raw_list$Gong@meta.data
df2 <- as.data.frame(table(meta$stage, meta$anno))
##plot###n_cells before QC
ggplot(data=df2, aes(x=Var1, y=Freq, fill=Var2)) +
geom_bar(stat="identity")+
theme(text=element_text(size=30))
#remove NAs
df <- raw_list$Gong@meta.data
table(df$anno)##
## Allantois AM Amnion Blood Border
## 9906 66 3765 1015 1976
## Cardiomyocyte CTB DE Endo EPI
## 3651 8626 430 1925 982
## Epidermis EVT EXM Foregut Hindgut
## 3695 2958 2630 429 381
## LPM Mes NA NeuralPlate ParE
## 9843 6740 59556 114 293
## PGC PM PS STB VE
## 1101 201 996 5329 1043
## ysMeso
## 6371cells_to_keep <- colnames(raw_list$Gong)[df$anno!="NA"]
raw_list$Gong<- subset(raw_list$Gong, cells = cells_to_keep)##Zhai_2023 use provided count matrix
###Zhai_2023
Zhai_2023_paper <- read.table("D:/xueying-work/afterPHD/Liu-lab/project1-embryo benchmarking/2024April/data/monkey-Zhai,2023/GSE218523_counts.csv/GSE218523_counts.csv", header = TRUE, sep = "\t", stringsAsFactors = FALSE)
colnames(Zhai_2023_paper)[1:5]## [1] "gene" "X20210303.YHY.1_sc1" "X20210303.YHY.1_sc10"
## [4] "X20210303.YHY.1_sc11" "X20210303.YHY.1_sc12"rownames(Zhai_2023_paper)[1:5]## [1] "1" "2" "3" "4" "5"rownames(Zhai_2023_paper) <- Zhai_2023_paper[,1]
Zhai_2023_paper <- Zhai_2023_paper[,-1,drop=FALSE]
anno2 <- read.csv("D:/xueying-work/afterPHD/Liu-lab/project1-embryo benchmarking/2024April/data/monkey-Zhai,2023/GSE218523_meta.csv", sep='\t')
Zhai_2023_paper <- CreateSeuratObject(counts = Zhai_2023_paper, project = "Zhai_2023_paper")## Warning: Data is of class data.frame. Coercing to dgCMatrix.Zhai_2023_paper[["percent.mt"]] <- PercentageFeatureSet(Zhai_2023_paper, pattern = "^MT.")
Idents(Zhai_2023_paper) <- "Zhai_2023"
VlnPlot(Zhai_2023_paper, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
anno2 <- anno2[match( colnames(Zhai_2023_paper), anno2$cells_id),]
Zhai_2023_paper$stage <- anno2$stage
Zhai_2023_paper$orig.ident <- "Zhai_2023"
Zhai_2023_paper$species <- "Macaca fascicularis"
Zhai_2023_paper$embryo <- anno2$embroy2
Zhai_2023_paper$platform <- "scChaRM-seq"
Zhai_2023_paper$anno <- anno2$celltype
raw_list$Zhai_2023 <- Zhai_2023_paper
names(raw_list)[8] <- "Zhai_2023_paper"
raw_list$Zhai_2023_paper$anno <- as.data.frame(str_split_fixed(raw_list$Zhai_2023_paper$anno, " ", 2))[,2]
VlnPlot(raw_list$Zhai_2023_paper, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
#filter cells with thresholds
metadata <- raw_list$Zhai_2023_paper@meta.data
cells_to_keep <- colnames(raw_list$Zhai_2023_paper)[metadata$nFeature_RNA > 500 & metadata$nCount_RNA > 1000 & metadata$percent.mt < 15]
raw_list$Zhai_2023_paper <- subset(raw_list$Zhai_2023_paper, cells = cells_to_keep)
VlnPlot(raw_list$Zhai_2023_paper, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)## Warning: Default search for "data" layer in "RNA" assay yielded no results;
## utilizing "counts" layer instead.
meta <- raw_list$Zhai_2023_paper@meta.data
df2 <- as.data.frame(table(meta$stage, meta$anno))
##plot###n_cells before QC
ggplot(data=df2, aes(x=Var1, y=Freq, fill=Var2)) +
geom_bar(stat="identity")+
theme(text=element_text(size=30))
##data correction
#check meta data
for (i in 1:length(raw_list)) {
cat("Column names of dataset",names(raw_list[i]) , ":\n")
print(colnames(raw_list[[i]]@meta.data))
cat("\n") # Print a newline for better readability between datasets
}## Column names of dataset Sasaki_paper :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "percent.mt" "stage"
## [6] "species" "embryo" "platform" "anno"
##
## Column names of dataset Nakamura_paper :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "percent.mt" "stage"
## [6] "species" "embryo" "platform" "anno"
##
## Column names of dataset Zhai_2022 :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "stage" "percent.mt"
## [6] "species" "embryo" "platform" "anno"
##
## Column names of dataset Ma_paper :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "stage" "anno"
## [6] "species" "embryo" "platform" "percent.mt"
##
## Column names of dataset Niu_paper :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "percent.mt" "stage"
## [6] "species" "embryo" "platform" "anno"
##
## Column names of dataset Yang :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "stage" "percent.mt"
## [6] "species" "embryo" "platform" "anno"
##
## Column names of dataset Gong :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "stage" "percent.mt"
## [6] "species" "embryo" "platform" "anno"
##
## Column names of dataset Zhai_2023_paper :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "percent.mt" "stage"
## [6] "species" "embryo" "platform" "anno"newlist <- c("orig.ident","nCount_RNA","nFeature_RNA","sample","stage", "percent.mt","species" , "embryo", "platform", "anno","anno_sub", "anno_harmo" )
# Iterate over each dataset in raw_list
for (i in 1:length(raw_list)) {
# Identify missing columns
missing_columns <- setdiff(newlist, colnames(raw_list[[i]]@meta.data))
# Add missing columns with NA values
for (col in missing_columns) {
raw_list[[i]]@meta.data[[col]] <- NA
}
# Reorder columns to match the desired order
raw_list[[i]]@meta.data <- raw_list[[i]]@meta.data[, newlist, drop = FALSE]
}
# Check the result by printing the column names of each dataset
for (i in 1:length(raw_list)) {
cat("Column names of unified dataset", names(raw_list[i]), ":\n")
print(colnames(raw_list[[i]]@meta.data))
cat("\n") # Print a newline for better readability between datasets
}## Column names of unified dataset Sasaki_paper :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "sample" "stage"
## [6] "percent.mt" "species" "embryo" "platform" "anno"
## [11] "anno_sub" "anno_harmo"
##
## Column names of unified dataset Nakamura_paper :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "sample" "stage"
## [6] "percent.mt" "species" "embryo" "platform" "anno"
## [11] "anno_sub" "anno_harmo"
##
## Column names of unified dataset Zhai_2022 :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "sample" "stage"
## [6] "percent.mt" "species" "embryo" "platform" "anno"
## [11] "anno_sub" "anno_harmo"
##
## Column names of unified dataset Ma_paper :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "sample" "stage"
## [6] "percent.mt" "species" "embryo" "platform" "anno"
## [11] "anno_sub" "anno_harmo"
##
## Column names of unified dataset Niu_paper :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "sample" "stage"
## [6] "percent.mt" "species" "embryo" "platform" "anno"
## [11] "anno_sub" "anno_harmo"
##
## Column names of unified dataset Yang :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "sample" "stage"
## [6] "percent.mt" "species" "embryo" "platform" "anno"
## [11] "anno_sub" "anno_harmo"
##
## Column names of unified dataset Gong :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "sample" "stage"
## [6] "percent.mt" "species" "embryo" "platform" "anno"
## [11] "anno_sub" "anno_harmo"
##
## Column names of unified dataset Zhai_2023_paper :
## [1] "orig.ident" "nCount_RNA" "nFeature_RNA" "sample" "stage"
## [6] "percent.mt" "species" "embryo" "platform" "anno"
## [11] "anno_sub" "anno_harmo"##correct stage names
#Nakamura
raw_list$Nakamura_paper$stage <- as.character(raw_list$Nakamura_paper$stage)
raw_list$Nakamura_paper$stage[raw_list$Nakamura_paper$stage == "E06"] <- "E6"
raw_list$Nakamura_paper$stage[raw_list$Nakamura_paper$stage == "E07"] <- "E7"
raw_list$Nakamura_paper$stage[raw_list$Nakamura_paper$stage == "E08"] <- "E8"
raw_list$Nakamura_paper$stage[raw_list$Nakamura_paper$stage == "E09"] <- "E9"
raw_list$Nakamura_paper$stage <- factor(raw_list$Nakamura_paper$stage)
#Zhai_2022
raw_list$Zhai_2022$embryo <- raw_list$Zhai_2022$stage
raw_list$Zhai_2022$stage[raw_list$Zhai_2022$stage %in% c("CS11-e1","CS11-e2","CS11-ys1") ] <- "CS11"
raw_list$Zhai_2022$stage[raw_list$Zhai_2022$stage %in% c("CS8-e1","CS8-e2") ] <- "CS8"
raw_list$Zhai_2022$stage[raw_list$Zhai_2022$stage %in% c("CS9-e1","CS9-e2") ] <- "CS9"
#Ma_paper
raw_list$Ma_paper$stage <- gsub("d.p.f.","E", raw_list$Ma_paper$stage )
#Niu_paper
raw_list$Niu_paper$stage <- gsub("E09","E9", raw_list$Niu_paper$stage )
#Yang_paper
raw_list$Yang$stage <- gsub("D", "E", raw_list$Yang$stage )
##add IVC label
raw_list$Ma_paper$stage <- paste(raw_list$Ma_paper$stage, "_IVC",sep = "")
raw_list$Niu_paper$stage <- paste(raw_list$Niu_paper$stage, "_IVC",sep = "")
raw_list$Yang$stage <- paste(raw_list$Yang$stage , "_IVC",sep = "")
raw_list$Zhai_2023_paper$stage <- paste(raw_list$Zhai_2023_paper$stage , "_IVC",sep = "")
raw_list$Gong$stage <- paste(raw_list$Gong$stage , "_IVC",sep = "")
#print out age
age <- do.call(rbind, lapply(1:length(raw_list), function(x) {
df <- as.data.frame(table(raw_list[[x]]$stage) )
df$dataset <- names(raw_list[x])
return(df)
}))
#check table
print(age)## Var1 Freq dataset
## 1 E13 3 Sasaki_paper
## 2 E14 1 Sasaki_paper
## 3 E16 11 Sasaki_paper
## 4 E17 2 Sasaki_paper
## 5 E20 8 Sasaki_paper
## 6 E13 46 Nakamura_paper
## 7 E14 62 Nakamura_paper
## 8 E16 36 Nakamura_paper
## 9 E17 53 Nakamura_paper
## 10 E6 43 Nakamura_paper
## 11 E7 35 Nakamura_paper
## 12 E8 95 Nakamura_paper
## 13 E9 20 Nakamura_paper
## 14 CS11 31314 Zhai_2022
## 15 CS8 14286 Zhai_2022
## 16 CS9 18975 Zhai_2022
## 17 E11_IVC 154 Ma_paper
## 18 E12_IVC 249 Ma_paper
## 19 E13_IVC 162 Ma_paper
## 20 E14_IVC 322 Ma_paper
## 21 E16_IVC 336 Ma_paper
## 22 E17_IVC 230 Ma_paper
## 23 E11_IVC 57 Niu_paper
## 24 E13_IVC 96 Niu_paper
## 25 E15_IVC 121 Niu_paper
## 26 E17_IVC 140 Niu_paper
## 27 E19_IVC 66 Niu_paper
## 28 E20_IVC 84 Niu_paper
## 29 E9_IVC 36 Niu_paper
## 30 E10_IVC 2847 Yang
## 31 E12_IVC 2919 Yang
## 32 E14_IVC 9737 Yang
## 33 E18_IVC 6742 Gong
## 34 E19_IVC 10602 Gong
## 35 E20_IVC 6422 Gong
## 36 E21_IVC 3854 Gong
## 37 E22_IVC 6475 Gong
## 38 E23_IVC 19659 Gong
## 39 E24_IVC 10881 Gong
## 40 E25_IVC 9831 Gong
## 41 E16_IVC 480 Zhai_2023_paper
## 42 E18_IVC 375 Zhai_2023_paper
## 43 E20_IVC 516 Zhai_2023_paper
## 44 E21_IVC 496 Zhai_2023_paper
## 45 E22_IVC 529 Zhai_2023_paper
## 46 E23_IVC 547 Zhai_2023_paper
## 47 E24_IVC 280 Zhai_2023_paper
## 48 E25_IVC 457 Zhai_2023_paper##organize label metadata
#####Sasaki_paper#############
unique(raw_list$Sasaki_paper$anno)## [1] "Gast" "ePGC"raw_list$Sasaki_paper$anno_harmo <- raw_list$Sasaki_paper$anno
raw_list$Sasaki_paper$anno_harmo[which(raw_list$Sasaki_paper$anno_harmo=="Gast")] <- "Primitive.streak"
raw_list$Sasaki_paper$anno_harmo[which(raw_list$Sasaki_paper$anno_harmo=="ePGC")] <- "PGC"
unique(raw_list$Sasaki_paper$anno_harmo)## [1] "Primitive.streak" "PGC"#####Nakamura_paper#############
unique(raw_list$Nakamura_paper$anno)## [1] "EXMC" "Gast1" "Gast2a" "Gast2b" "Hypoblast" "ICM"
## [7] "Post-paTE" "PostE-EPI" "PostL-EPI" "Pre-EPI" "PreE-TE" "PreL-TE"
## [13] "VE/YE"raw_list$Nakamura_paper$anno_harmo <- raw_list$Nakamura_paper$anno
raw_list$Nakamura_paper$anno_harmo[grep("Gast", raw_list$Nakamura_paper$anno_harmo)] <- "Primitive.streak"
raw_list$Nakamura_paper$anno_harmo[which(raw_list$Nakamura_paper$anno_harmo=="EXMC")] <- "ExE.Mesoderm"
raw_list$Nakamura_paper$anno_harmo[grep("TE", raw_list$Nakamura_paper$anno)] <- "TE/TrB"
raw_list$Nakamura_paper$anno_harmo[grep("EPI", raw_list$Nakamura_paper$anno)] <- "Epiblast"
raw_list$Nakamura_paper$anno_harmo[grep("VE/YE", raw_list$Nakamura_paper$anno)] <- "ExE.Endoderm"
#####Zhai_2022#############
unique(raw_list$Zhai_2022$anno)## [1] "Nas.Meso" "EPI" "Gut" "ECT" "NA"
## [6] "PGC" "Mes" "VE" "ys.Endo1" "APS"
## [11] "AM" "LP.Meso" "AI" "ys.Endo2" "EC"
## [16] "DE" "ExE.Meso" "ys.Meso2" "SE2" "SE1"
## [21] "Pharyg.Meso" "ys.Meso1" "PS" "Caud.Meso" "Node"
## [26] "NC" "BP" "Ery2" "Ery1" "Mac"
## [31] "Para.Meso" "SC" "FB/MB/HB" "Cardi.Meso" "Rostr.Meso"
## [36] "NMP" "Cardi. " "Inter.Meso" "PSM"raw_list$Zhai_2022$anno_harmo <- raw_list$Zhai_2022$anno
raw_list$Zhai_2022$anno_harmo[which(raw_list$Zhai_2022$anno=="EPI")] <- "Epiblast"
raw_list$Zhai_2022$anno_harmo[which(raw_list$Zhai_2022$anno %in% c("Gut", "DE") )] <- "Endoderm"
raw_list$Zhai_2022$anno_harmo[which(raw_list$Zhai_2022$anno %in% c("ECT", "AM","SE1", "SE2") )] <- "Non-Neural.Ectoderm"
raw_list$Zhai_2022$anno_harmo[which(raw_list$Zhai_2022$anno %in% c("NC", "SC","FB/MB/HB") )] <- "Neural.Ectoderm"
raw_list$Zhai_2022$anno_harmo[which(raw_list$Zhai_2022$anno=="PGC")] <- "PGC"
raw_list$Zhai_2022$anno_harmo[which(raw_list$Zhai_2022$anno %in% c("VE", "ys.Endo1","ys.Endo2") )] <- "ExE.Endoderm"
raw_list$Zhai_2022$anno_harmo[which(raw_list$Zhai_2022$anno %in% c("APS", "PS","NMP") )] <- "Primitive.streak"
raw_list$Zhai_2022$anno_harmo[which(raw_list$Zhai_2022$anno=="Node")] <- "Node"
raw_list$Zhai_2022$anno_harmo[which(raw_list$Zhai_2022$anno %in% c("BP","Ery2", "Ery1","Mac","EC") )] <- "Hemogenic.Endothelial.Progenitors"
raw_list$Zhai_2022$anno_harmo[which(raw_list$Zhai_2022$anno %in% c("ys.Meso1","ys.Meso2","AI","ExE.Meso") )] <- "ExE.Mesoderm"
raw_list$Zhai_2022$anno_harmo[which(raw_list$Zhai_2022$anno %in% c("Mes","Nas.Meso", "LP.Meso","Pharyg.Meso","Caud.Meso", "Para.Meso", "Cardi.Meso","Rostr.Meso", "Cardi. ","Inter.Meso","PSM") )] <- "Mesoderm"
raw_list$Zhai_2022$anno_harmo[which(raw_list$Zhai_2022$anno=="NA")] <- "Unknown"
##Ma_paper######
unique(raw_list$Ma_paper$anno)## [1] "TE and derivatives" "postE-EPI" "EXMC"
## [4] "VE/YE" "E-Gast" "E-AM"
## [7] "L-Gast1" "L-Gast2" "L-AM2"
## [10] "postL-EPI" "L-AM1" "E-PGC"raw_list$Ma_paper$anno_harmo <- raw_list$Ma_paper$anno
raw_list$Ma_paper$anno_harmo[which(raw_list$Ma_paper$anno=="TE and derivatives")] <- "TE/TrB"
raw_list$Ma_paper$anno_harmo[grep("EPI", raw_list$Ma_paper$anno)] <- "Epiblast"
raw_list$Ma_paper$anno_harmo[which(raw_list$Ma_paper$anno=="EXMC")] <- "ExE.Mesoderm"
raw_list$Ma_paper$anno_harmo[which(raw_list$Ma_paper$anno=="VE/YE")] <- "ExE.Endoderm"
raw_list$Ma_paper$anno_harmo[grep("Gast", raw_list$Ma_paper$anno)] <- "Primitive.streak"
raw_list$Ma_paper$anno_harmo[which(raw_list$Ma_paper$anno %in% c("E-AM","L-AM2","L-AM1") )] <- "Non-Neural.Ectoderm"
raw_list$Ma_paper$anno_harmo[which(raw_list$Ma_paper$anno=="E-PGC")] <- "PGC"
##Niu_paper####
unique(raw_list$Niu_paper$anno)## [1] "NA" "PE" "Epi" "EXMC" "TE"raw_list$Niu_paper$anno_harmo <- raw_list$Niu_paper$anno
raw_list$Niu_paper$anno_harmo[which(raw_list$Niu_paper$anno=="PE")] <- "Hypoblast"
raw_list$Niu_paper$anno_harmo[which(raw_list$Niu_paper$anno=="Epi")] <- "Epiblast"
raw_list$Niu_paper$anno_harmo[which(raw_list$Niu_paper$anno=="EXMC")] <- "ExE.Mesoderm"
raw_list$Niu_paper$anno_harmo[which(raw_list$Niu_paper$anno=="TE")] <- "TE/TrB"
raw_list$Niu_paper$anno_harmo[which(raw_list$Niu_paper$anno=="NA")] <- "Unknown"
##Yang####
unique(raw_list$Yang$anno)## [1] "PE" "TE" "Epi" "EXMC"raw_list$Yang$anno_harmo <- raw_list$Yang$anno
raw_list$Yang$anno_harmo[which(raw_list$Yang$anno=="PE")] <- "Hypoblast"
raw_list$Yang$anno_harmo[which(raw_list$Yang$anno=="TE")] <- "TE/TrB"
raw_list$Yang$anno_harmo[which(raw_list$Yang$anno=="Epi")] <- "Epiblast"
raw_list$Yang$anno_harmo[which(raw_list$Yang$anno=="EXMC")] <- "ExE.Mesoderm"
##Zhai_2023_paper###
unique(raw_list$Zhai_2023_paper$anno)## [1] "PS and derivative" "EXMC"
## [3] "EPI" "Extraembryonic Mesoderm"
## [5] "EVT" "Mesoderm"
## [7] "Endoderm" "STB"
## [9] "CTB" "Non-neural ectoderm"
## [11] "Blood cells" "Neural ectoderm"raw_list$Zhai_2023_paper$anno_harmo <- raw_list$Zhai_2023_paper$anno
raw_list$Zhai_2023_paper$anno_harmo[which(raw_list$Zhai_2023_paper$anno=="PS and derivative")] <- "Primitive.streak"
raw_list$Zhai_2023_paper$anno_harmo[which(raw_list$Zhai_2023_paper$anno=="EXMC")] <- "ExE.Mesoderm"
raw_list$Zhai_2023_paper$anno_harmo[which(raw_list$Zhai_2023_paper$anno=="EPI")] <- "Epiblast"
raw_list$Zhai_2023_paper$anno_harmo[which(raw_list$Zhai_2023_paper$anno=="Extraembryonic Mesoderm")] <- "ExE.Mesoderm"
raw_list$Zhai_2023_paper$anno_harmo[which(raw_list$Zhai_2023_paper$anno %in% c("EVT", "STB", "CTB"))] <- "TE/TrB"
raw_list$Zhai_2023_paper$anno_harmo[which(raw_list$Zhai_2023_paper$anno=="Non-neural ectoderm")] <- "Non-Neural.Ectoderm"
raw_list$Zhai_2023_paper$anno_harmo[which(raw_list$Zhai_2023_paper$anno=="Blood cells")] <- "Hemogenic.Endothelial.Progenitors"
raw_list$Zhai_2023_paper$anno_harmo[which(raw_list$Zhai_2023_paper$anno=="Neural ectoderm")] <- "Neural.Ectoderm"
raw_list$Zhai_2023_paper$anno_harmo[which(raw_list$Zhai_2023_paper$anno=="Endoderm")] <- "Endoderm"
raw_list$Zhai_2023_paper$anno_harmo[which(raw_list$Zhai_2023_paper$anno=="Mesoderm")] <- "Mesoderm"
##Gong####
unique(raw_list$Gong$anno)## [1] "LPM" "ysMeso" "EXM" "Allantois"
## [5] "PS" "Endo" "DE" "Mes"
## [9] "Cardiomyocyte" "VE" "Border" "PM"
## [13] "STB" "Amnion" "CTB" "Hindgut"
## [17] "Epidermis" "EVT" "EPI" "AM"
## [21] "PGC" "ParE" "NeuralPlate" "Blood"
## [25] "Foregut"raw_list$Gong$anno_harmo <- raw_list$Gong$anno
raw_list$Gong$anno_harmo[which(raw_list$Gong$anno %in% c("ysMeso", "EXM", "Allantois") )] <- "ExE.Mesoderm"
raw_list$Gong$anno_harmo[which(raw_list$Gong$anno=="PS" )] <- "Primitive.streak"
raw_list$Gong$anno_harmo[which(raw_list$Gong$anno %in% c("Endo", "Blood" ) )] <- "Hemogenic.Endothelial.Progenitors"
raw_list$Gong$anno_harmo[which(raw_list$Gong$anno %in% c( "DE", "Hindgut","Foregut") )] <- "Endoderm"
raw_list$Gong$anno_harmo[which(raw_list$Gong$anno %in% c("VE","ParE") )] <- "ExE.Endoderm"
raw_list$Gong$anno_harmo[which(raw_list$Gong$anno %in% c("CTB","STB","EVT") )] <- "TE/TrB"
raw_list$Gong$anno_harmo[which(raw_list$Gong$anno=="PGC")] <- "PGC"
raw_list$Gong$anno_harmo[which(raw_list$Gong$anno %in% c( "NeuralPlate" ) )] <- "Neural.Ectoderm"
raw_list$Gong$anno_harmo[which(raw_list$Gong$anno %in% c("EPI") )] <- "Epiblast"
raw_list$Gong$anno_harmo[which(raw_list$Gong$anno %in% c("Border","Amnion","Epidermis") )] <- "Non-Neural.Ectoderm"
raw_list$Gong$anno_harmo[which(raw_list$Gong$anno %in% c("LPM", "Mes", "Cardiomyocyte","PM","AM") )] <- "Mesoderm"
#add anno_sub
raw_list$Sasaki_paper$anno_sub <- raw_list$Sasaki_paper$anno
raw_list$Nakamura_paper$anno_sub <- raw_list$Nakamura_paper$anno
raw_list$Zhai_2022$anno_sub <- raw_list$Zhai_2022$anno
raw_list$Ma_paper$anno_sub <- raw_list$Ma_paper$anno
raw_list$Yang$anno_sub <- raw_list$Yang$anno
raw_list$Gong$anno_sub <- raw_list$Gong$anno
raw_list$Niu_paper$anno_sub <- raw_list$Niu_paper$anno
raw_list$Zhai_2023_paper$anno_sub <- raw_list$Zhai_2023_paper$anno#generate label metadata
##Sasaki########
df_Sasaki<- data.frame("dataset"= rep(c("Sasaki")),
"stage" = rep(c("E13-E20")),
"orig.anno" = raw_list$Sasaki_paper$anno,
"orig.sub_anno" = raw_list$Sasaki_paper$anno_sub,
"harmo.anno" = raw_list$Sasaki_paper$anno_harmo )
df_Sasaki<- unique(df_Sasaki)
##Nakamura#########################
df_Nakamura <- data.frame("dataset"= rep(c("Nakamura")),
"stage" = rep(c("E6-E17")),
"orig.anno" = raw_list$Nakamura_paper$anno,
"orig.sub_anno" = raw_list$Nakamura_paper$anno_sub,
"harmo.anno" = raw_list$Nakamura_paper$anno_harmo )
df_Nakamura<- unique(df_Nakamura)
##Zhai_2022#########################
df_Zhai_2022<- data.frame("dataset"= rep(c("Zhai_2022")),
"stage" = rep(c("E20-E29")),
"orig.anno" = raw_list$Zhai_2022$anno,
"orig.sub_anno" = raw_list$Zhai_2022$anno_sub,
"harmo.anno" = raw_list$Zhai_2022$anno_harmo )
df_Zhai_2022<- unique(df_Zhai_2022)
##Ma#########################
df_Ma<- data.frame("dataset"= rep(c("Ma")),
"stage" = rep(c("E11-E17")),
"orig.anno" = raw_list$Ma_paper$anno,
"orig.sub_anno" = raw_list$Ma_paper$anno_sub,
"harmo.anno" = raw_list$Ma_paper$anno_harmo )
df_Ma<- unique(df_Ma)
##Niu#########################
df_Niu<- data.frame("dataset"= rep(c("Niu")),
"stage" = rep(c("E9-E20")),
"orig.anno" = raw_list$Niu_paper$anno,
"orig.sub_anno" = raw_list$Niu_paper$anno_sub,
"harmo.anno" = raw_list$Niu_paper$anno_harmo )
df_Niu<- unique(df_Niu)
##Yang#########################
df_Yang<- data.frame("dataset"= rep(c("Yang")),
"stage" = rep(c("E10-E14")),
"orig.anno" = raw_list$Yang$anno,
"orig.sub_anno" = raw_list$Yang$anno_sub,
"harmo.anno" = raw_list$Yang$anno_harmo )
df_Yang<- unique(df_Yang)
##Gong#########################
df_Gong<- data.frame("dataset"= rep(c("Gong")),
"stage" = rep(c("E18-E25")),
"orig.anno" = raw_list$Gong$anno,
"orig.sub_anno" = raw_list$Gong$anno_sub,
"harmo.anno" = raw_list$Gong$anno_harmo )
df_Gong<- unique(df_Gong)
##Zhai_2023#########################
df_Zhai_2023_paper<- data.frame("dataset"= rep(c("Zhai_2023")),
"stage" = rep(c("E16-E25")),
"orig.anno" = raw_list$Zhai_2023_paper$anno,
"orig.sub_anno" = raw_list$Zhai_2023_paper$anno_sub,
"harmo.anno" = raw_list$Zhai_2023_paper$anno_harmo )
df_Zhai_2023_paper<- unique(df_Zhai_2023_paper)
#############merge#########################
label_all <- rbind(df_Sasaki,df_Nakamura,df_Zhai_2022,df_Ma,df_Niu,df_Yang,df_Gong,df_Zhai_2023_paper )
unique(label_all$harmo.anno)## [1] "Primitive.streak" "PGC"
## [3] "ExE.Mesoderm" "Hypoblast"
## [5] "ICM" "TE/TrB"
## [7] "Epiblast" "ExE.Endoderm"
## [9] "Mesoderm" "Endoderm"
## [11] "Non-Neural.Ectoderm" "Unknown"
## [13] "Hemogenic.Endothelial.Progenitors" "Node"
## [15] "Neural.Ectoderm"#check cells
# check cell numbers
cell_counts <- data.frame(Dataset = integer(), Cell_Count = integer())
for (i in seq_along(raw_list)) {
# Get the number of cells in the current Seurat object
cell_count <- ncol(raw_list[[i]])
# Store the result in the data frame
cell_counts <- rbind(cell_counts, data.frame(Dataset = names(raw_list[i]), Cell_Count = cell_count))
}
# Print the table of cell counts
print(cell_counts)## Dataset Cell_Count
## 1 Sasaki_paper 25
## 2 Nakamura_paper 390
## 3 Zhai_2022 64575
## 4 Ma_paper 1453
## 5 Niu_paper 600
## 6 Yang 15503
## 7 Gong 74466
## 8 Zhai_2023_paper 3680print(sum(cell_counts$Cell_Count))## [1] 160692# Check QC
# Initialize an empty list to store the data frames
df_list <- lapply(seq_along(raw_list), function(i) {
# Create a data frame with min and max values for QC metrics
df <- data.frame(
min_nCount_RNA = min(raw_list[[i]]$nCount_RNA),
min_nFeature_RNA = min(raw_list[[i]]$nFeature_RNA),
max_percent_mt = max(raw_list[[i]]$percent.mt)
)
# Add the dataset name as a column
df$dataset <- names(raw_list)[i]
return(df)
})
# Combine all data frames into one
df <- do.call(rbind, df_list)
# Print the table of cell counts
print(df)## min_nCount_RNA min_nFeature_RNA max_percent_mt dataset
## 1 39449.43 9040 0.3960381 Sasaki_paper
## 2 999727.03 7802 0.3392961 Nakamura_paper
## 3 1002.00 502 8.3025558 Zhai_2022
## 4 3376.00 2018 0.8921330 Ma_paper
## 5 19254.98 2088 0.8626626 Niu_paper
## 6 1002.00 501 1.9547936 Yang
## 7 1979.00 641 6.7808708 Gong
## 8 1052.00 510 1.3338371 Zhai_2023_papersave Rds
setwd("D:/xueying-work/afterPHD/Liu-lab/project1-embryo benchmarking/2024April/manuscript/R runing")
outputDir = getwd()
write.xlsx(label_all, file = "monkey_pre-label.xlsx")
for (i in 1:length(raw_list)) {
colnames(raw_list[[i]]@meta.data) <- gsub("anno_sub", "sub_anno", colnames(raw_list[[i]]@meta.data) )
colnames(raw_list[[i]]@meta.data) <- gsub("anno_harmo", "harmo.anno", colnames(raw_list[[i]]@meta.data) )
}
saveRDS(raw_list, file = "Monkey_all.rds")remove Sasaki dataset
##removeSasaki dataset due to only 25 cells left
monkey <- readRDS("monkey_all.rds")
monkey <- monkey[!names(monkey) %in% "Sasaki_paper"]
saveRDS(monkey, file = "Monkey_all.rds")session info
session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 4.3.3 (2024-02-29 ucrt)
## os Windows 11 x64 (build 22631)
## system x86_64, mingw32
## ui RTerm
## language (EN)
## collate Chinese (Simplified)_China.utf8
## ctype Chinese (Simplified)_China.utf8
## tz Asia/Shanghai
## date 2024-09-02
## pandoc 3.1.11 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## ! package * version date (UTC) lib source
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##
## [1] C:/Users/fxyin/AppData/Local/R/win-library/4.3
## [2] C:/Program Files/R/R-4.3.3/library
##
## D ── DLL MD5 mismatch, broken installation.
## R ── Package was removed from disk.
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